Journal articles on the topic 'Rail temperature forecast'
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Li, Xin Can. "Organizational Field Forecast during Heavy Rail Quenching Process by Air Cooling." Applied Mechanics and Materials 494-495 (February 2014): 697–700. http://dx.doi.org/10.4028/www.scientific.net/amm.494-495.697.
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Organizational field is a vital parameter in quenching process. the phase changing temperature of steel U71Mn was got based on its CCT curves. Through cooling curves of several key points, the cooling rate at phase transition point was calculated. By comparing with every microstructures critical cooling rate, the final cooling microstructure was predicted. Relative tests showed that the prediction was reasonable.
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Liu, Zhong Min, Jin Long Yan, and Jian Feng Li. "Research on NOx Emission Characteristics of Medium Speed Diesel Engine." Advanced Materials Research 744 (August 2013): 215–18. http://dx.doi.org/10.4028/www.scientific.net/amr.744.215.
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In order to research the NOx emission characteristics of the medium speed diesel engine DN8340, a finite element model is built. Calculate the influence of changing the intake charge temperature and injection timing on NOx emission, and forecast how it works to decrease the intake charge temperature and delay injection timing by the methods of Miller-cycle, oscillating cooling piston and electronic controlled high pressure common rail. The calculation result shows that Decreasing the intake charge temperature and delaying the injection timing can reduce the NOx emission to 2.22g/kWh effectively which meets the requirement of IMO TierIII.
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Zhang, Yan Qin, Li Guo Fan, Yao Chen, Rui Li, Tian Zheng Wu, and Xiao Dong Yu. "Deformation Analysis of Hydrostatic Thrust Bearing under Different Load." Applied Mechanics and Materials 494-495 (February 2014): 583–86. http://dx.doi.org/10.4028/www.scientific.net/amm.494-495.583.
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In order to solve deformation problem of large size hydrostatic thrust bearing under different load. Simulation model of fan cavity hydrostatic thrust bearing, three-dimensional mathematical model in guide rail is set up. Using CFD principle, FIUENT and ANSYS Workbench software, temperature field, pressure field and deformation field of hydrostatic bearing is simulated. Hydrostatic bearing deformation under different load conditions can be simulated and the lubrication characteristic is forecast in advance through this method. It provides valuable theoretical basis for hydrostatic bearing structure parameter optimization of oil cavity and worktable.
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Feng, Guoce, Lei Zhang, Feifan Ai, Yirui Zhang, and Yupeng Hou. "An Improved Temporal Fusion Transformers Model for Predicting Supply Air Temperature in High-Speed Railway Carriages." Entropy 24, no. 8 (August 12, 2022): 1111. http://dx.doi.org/10.3390/e24081111.
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A key element for reducing energy consumption and improving thermal comfort on high-speed rail is controlling air-conditioning temperature. Accurate prediction of air supply temperature is aimed at improving control effects. Existing studies of supply air temperature prediction models are interdisciplinary, involving heat transfer science and computer science, where the problem is defined as time-series prediction. However, the model is widely accepted as a complex model that is nonlinear and dynamic. That makes it difficult for existing statistical and deep learning methods, e.g., autoregressive integrated moving average model (ARIMA), convolutional neural network (CNN), and long short-term memory network (LSTM), to fully capture the interaction between these variables and provide accurate prediction results. Recent studies have shown the potential of the Transformer to increase the prediction capacity. This paper offers an improved temporal fusion transformers (TFT) prediction model for supply air temperature in high-speed train carriages to tackle these challenges, with two improvements: (i) Double-convolutional residual encoder structure based on dilated causal convolution; (ii) Spatio-temporal double-gated structure based on Gated Linear Units. Moreover, this study designs a loss function suitable for general long sequence time-series forecast tasks for temperature forecasting. Empirical simulations using a high-speed rail air-conditioning operation dataset at a specific location in China show that the temperature prediction of the two units using the improved TFT model improves the MAPE by 21.70% and 11.73%, respectively the original model. Furthermore, experiments demonstrate that the model effectively outperforms seven popular methods on time series computing tasks, and the attention of the prediction problem in the time dimension is analyzed.
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Gómez, I., S. Molina, J. Olcina, and J. J. Galiana-Merino. "Perceptions, Uses, and Interpretations of Uncertainty in Current Weather Forecasts by Spanish Undergraduate Students." Weather, Climate, and Society 13, no. 1 (January 2021): 83–94. http://dx.doi.org/10.1175/wcas-d-20-0048.1.
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AbstractThis quantitative study evaluates how 71 Spanish undergraduate students perceive and interpret the uncertainty inherent to deterministic forecasts. It is based on several questions that asked participants what they expect given a forecast presented under the deterministic paradigm for a specific lead time and a particular weather parameter. In this regard, both normal and extreme weather conditions were studied. Students’ responses to the temperature forecast as it is usually presented in the media expect an uncertainty range of ±1°–2°C. For wind speed, uncertainty shows a deviation of ±5–10 km h−1, and the uncertainty range assigned to the precipitation amount shows a deviation of ±30 mm from the specific value provided in a deterministic format. Participants perceive the minimum night temperatures as the least-biased parameter from the deterministic forecast, while the amount of rain is perceived as the most-biased one. In addition, participants were then asked about their probabilistic threshold for taking appropriate precautionary action under distinct decision-making scenarios of temperature, wind speed, and rain. Results indicate that participants have different probabilistic thresholds for taking protective action and that context and presentation influence forecast use. Participants were also asked about the meaning of the probability-of-precipitation (PoP) forecast. Around 40% of responses reformulated the default options, and around 20% selected the correct answer, following previous studies related to this research topic. As a general result, it has been found that participants infer uncertainty into deterministic forecasts, and they are mostly used to take action in the presence of decision-making scenarios. In contrast, more difficulties were found when interpreting probabilistic forecasts.
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Scheuerer, Michael, Scott Gregory, Thomas M. Hamill, and Phillip E. Shafer. "Probabilistic Precipitation-Type Forecasting Based on GEFS Ensemble Forecasts of Vertical Temperature Profiles." Monthly Weather Review 145, no. 4 (March 21, 2017): 1401–12. http://dx.doi.org/10.1175/mwr-d-16-0321.1.
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Abstract A Bayesian classification method for probabilistic forecasts of precipitation type is presented. The method considers the vertical wet-bulb temperature profiles associated with each precipitation type, transforms them into their principal components, and models each of these principal components by a skew normal distribution. A variance inflation technique is used to de-emphasize the impact of principal components corresponding to smaller eigenvalues, and Bayes’s theorem finally yields probability forecasts for each precipitation type based on predicted wet-bulb temperature profiles. This approach is demonstrated with reforecast data from the Global Ensemble Forecast System (GEFS) and observations at 551 METAR sites, using either the full ensemble or the control run only. In both cases, reliable probability forecasts for precipitation type being either rain, snow, ice pellets, freezing rain, or freezing drizzle are obtained. Compared to the model output statistics (MOS) approach presently used by the National Weather Service, the skill of the proposed method is comparable for rain and snow and significantly better for the freezing precipitation types.
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DeGaetano, Arthur T., Brian N. Belcher, and Pamela L. Spier. "Short-Term Ice Accretion Forecasts for Electric Utilities Using the Weather Research and Forecasting Model and a Modified Precipitation-Type Algorithm." Weather and Forecasting 23, no. 5 (October 1, 2008): 838–53. http://dx.doi.org/10.1175/2008waf2006106.1.
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Abstract The Weather Research and Forecasting model (WRF) is used to provide 6–12-h forecasts of the necessary input parameters to a separate algorithm that determines the most likely precipitation type at each model grid point. In instances where freezing rain is indicated, an ice accretion model allows forecasts of radial ice thickness to be developed. The resulting forecasts are evaluated for 38 icing events of varying magnitude that occurred in the eastern United States using National Weather Service storm impact reports and observed data from Automated Surface Observing Systems (ASOS). Ice accretion hindcasts, using the WRF, allow the development of climatologies based on archived model initialization data. Ice accretion forecasts, based on the Ramer precipitation-type algorithm, consistently underestimated the maximum observed ice accretion amounts by between 10 and 20 mm. Ice accretion at ASOS sites was also underestimated. Applying a modification to the Ramer precipitation-type algorithm, and focusing on the thermal profile below the lowest 0°C isotherm, improved the ice accretion forecasts, but still underestimated the maximum ice thickness. Little bias was evident in ice accretion forecasts for the ASOS sites. Using previous observations from outside the forecast window to account for WRF and precipitation-type algorithm biases in precipitation amount, wind speed, temperature, and precipitation type provided some forecast improvement. The forecast procedure using the modified Ramer precipitation algorithm captures both the magnitude and extent of icing in both widespread severe icing events and localized storms. Minimal icing is indicated in events and at locations where precipitation fell as rain or snow.
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Lopez, Philippe. "Experimental 4D-Var Assimilation of SYNOP Rain Gauge Data at ECMWF." Monthly Weather Review 141, no. 5 (May 1, 2013): 1527–44. http://dx.doi.org/10.1175/mwr-d-12-00024.1.
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Abstract Four-dimensional variational data assimilation (4D-Var) experiments with 6-hourly rain gauge accumulations observed at synoptic stations (SYNOP) around the globe have been run over several months, both at high resolution in an ECMWF operations-like framework and at lower resolution with the reference observational coverage reduced to surface pressure data only, as would be expected in early twentieth-century periods. The key aspects of the technical implementation of rain gauge data assimilation in 4D-Var are described, which include the specification of observation errors, bias correction procedures, screening, and quality control. Results from experiments indicate that the positive impact of rain gauges on forecast scores remains limited in the operations-like context because of their competition with all other observations already available. In contrast, when only synoptic station surface pressure observations are assimilated in the data-poor control experiment, the additional assimilation of rain gauge measurements substantially improves not only surface precipitation scores, but also analysis and forecast scores of temperature, geopotential, wind, and humidity at most atmospheric levels and for forecast ranges up to 10 days. The verification against Meteosat infrared imagery also shows a slight improvement in the spatial distribution of clouds. This suggests that assimilating rain gauge data available during data-sparse periods of the past might help to improve the quality of future reanalyses and subsequent forecasts.
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Al-Jiboori, Monim, Mahmoud Jawad Abu Al-Shaeer, and Ahemd S. Hassan. "Statistical Forecast of Daily Maximum Air Temperature in Arid Areas at Summertime." Journal of Mathematical and Fundamental Sciences 52, no. 3 (December 31, 2020): 353–65. http://dx.doi.org/10.5614/j.math.fund.sci.2020.52.3.8.
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Based on historical observations of summers for the period from 2004 to 2018 with a focus on daily maximum and minimum air temperatures and wind speed recorded at 0600 GMT, a non-linear regression hypothesis is developed for forecasting daily maximum air temperature (Tmax) in arid areas such as Baghdad International airport station, which has a hot climate with no cloud cover or rain. Observations with dust storm events were excluded, thus this hypothesis could be used to predict daily Tmax on any day during summers characterized by fair weather. Using mean annual daily temperature range, daily minimum temperature, and the trend of maximum temperature with wind speed, Tmax was forecasted and then compared to those recorded by meteorological instruments. To improve the accuracy of the hypothesis, daily forecast errors, bias, and mean absolute error were analyzed to detect their characteristics through calculating relative frequencies of occurrence. At the end of this analysis, a value of (-0.45ºC) was added to the hypothesis as a bias term.
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Hung, N. Q., M. S. Babel, S. Weesakul, and N. K. Tripathi. "An artificial neural network model for rainfall forecasting in Bangkok, Thailand." Hydrology and Earth System Sciences 13, no. 8 (August 7, 2009): 1413–25. http://dx.doi.org/10.5194/hess-13-1413-2009.
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Abstract. This paper presents a new approach using an Artificial Neural Network technique to improve rainfall forecast performance. A real world case study was set up in Bangkok; 4 years of hourly data from 75 rain gauge stations in the area were used to develop the ANN model. The developed ANN model is being applied for real time rainfall forecasting and flood management in Bangkok, Thailand. Aimed at providing forecasts in a near real time schedule, different network types were tested with different kinds of input information. Preliminary tests showed that a generalized feedforward ANN model using hyperbolic tangent transfer function achieved the best generalization of rainfall. Especially, the use of a combination of meteorological parameters (relative humidity, air pressure, wet bulb temperature and cloudiness), the rainfall at the point of forecasting and rainfall at the surrounding stations, as an input data, advanced ANN model to apply with continuous data containing rainy and non-rainy period, allowed model to issue forecast at any moment. Additionally, forecasts by ANN model were compared to the convenient approach namely simple persistent method. Results show that ANN forecasts have superiority over the ones obtained by the persistent model. Rainfall forecasts for Bangkok from 1 to 3 h ahead were highly satisfactory. Sensitivity analysis indicated that the most important input parameter besides rainfall itself is the wet bulb temperature in forecasting rainfall.
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Witmer, Frank DW, Andrew M. Linke, John O’Loughlin, Andrew Gettelman, and Arlene Laing. "Subnational violent conflict forecasts for sub-Saharan Africa, 2015–65, using climate-sensitive models." Journal of Peace Research 54, no. 2 (February 22, 2017): 175–92. http://dx.doi.org/10.1177/0022343316682064.
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How will local violent conflict patterns in sub-Saharan Africa evolve until the middle of the 21st century? Africa is recognized as a particularly vulnerable continent to environmental and climate change since a large portion of its population is poor and reliant on rain-fed agriculture. We use a climate-sensitive approach to model sub-Saharan African violence in the past (geolocated to the nearest settlements) and then forecast future violence using sociopolitical factors such as population size and political rights (governance), coupled with temperature anomalies. Our baseline model is calibrated using 1° gridded monthly data from 1980 to 2012 at a finer spatio-temporal resolution than existing conflict forecasts. We present multiple forecasts of violence under alternative climate change scenarios (optimistic and current global trajectories), of political rights scenarios (improvement and decline), and population projections (low and high fertility). We evaluate alternate shared socio-economic pathways (SSPs) by plotting violence forecasts over time and by detailed mapping of recent and future levels of violence by decade. The forecasts indicate that a growing population and rising temperatures will lead to higher levels of violence in sub-Saharan Africa if political rights do not improve. If political rights continue to improve at the same rate as observed over the last three decades, there is reason for optimism that overall levels of violence will hold steady or even decline in Africa, in spite of projected population increases and rising temperatures.
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Steppeler, J., H. W. Bitzer, Z. Janjic, U. Schättler, P. Prohl, U. Gjertsen, L. Torrisi, J. Parfinievicz, E. Avgoustoglou, and U. Damrath. "Prediction of Clouds and Rain Using a z-Coordinate Nonhydrostatic Model." Monthly Weather Review 134, no. 12 (December 1, 2006): 3625–43. http://dx.doi.org/10.1175/mwr3331.1.
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Abstract The most common option for numerical models of the atmosphere is to use model layers following the surface of the earth, using a terrain-following vertical coordinate. The present paper investigates the forecast of clouds and precipitation using the z-coordinate nonhydrostatic version of the Lokalmodell (LM-z). This model uses model layers that are parallel to the surface of the sphere and consequently intersect the orography. Physical processes are computed on a special grid, allowing adequate grid spacing even over high mountains. In other respects the model is identical to the nonhydrostatic terrain-following version of the LM, which in a number of European countries is used for operational mesoscale forecasting. The terrain-following version of the LM (LM-tf) is used for comparison with the forecasts of the LM-z. Terrain-following coordinates are accurate when the orography is shallow and smooth, while z-coordinate models need not satisfy this condition. Because the condition of smooth orography is rarely satisfied in reality, z-coordinate models should lead to a better representation of the atmospheric flow near mountains and consequently to a better representation of fog, low stratus, and precipitation. A number of real-data cases, computed with a grid spacing of 7 and 14 km, are investigated. A total of 39 real-data cases have been used to evaluate forecast scores. A rather systematic improvement of precipitation forecasts resulted in a substantial increase of threat scores. Furthermore, RMS verification against radiosondes showed an improvement of the 24-h forecast, both for wind and temperature. To investigate the possibility of flow separation at mountain tops, the flow in the lee of southern Italy was investigated.
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Nyadzi, Emmanuel, E. Saskia Werners, Robbert Biesbroek, Phi Hoang Long, Wietse Franssen, and Fulco Ludwig. "Verification of Seasonal Climate Forecast toward Hydroclimatic Information Needs of Rice Farmers in Northern Ghana." Weather, Climate, and Society 11, no. 1 (December 3, 2018): 127–42. http://dx.doi.org/10.1175/wcas-d-17-0137.1.
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Abstract Farmers in sub-Saharan Africa face many difficulties when making farming decisions due to unexpected changes in weather and climate. Access to hydroclimatic information can potentially assist farmers to adapt. This study explores the extent to which seasonal climate forecasts can meet hydroclimatic information needs of rice farmers in northern Ghana. First, 62 rice farmers across 12 communities were interviewed about their information needs. Results showed that importance of hydroclimatic information depends on the frequency of use and farming type (rain-fed, irrigated, or both). Generally, farmers perceived rainfall distribution, dam water level, and temperature as very important information, followed by total rainfall amount and onset ranked as important. These findings informed our skills assessment of rainfall (Prcp), minimum temperature (Tmin), and maximum temperature (Tmax) from the European Centre for Medium-Range Weather Forecasts (ECMWF-S4) and at lead times of 0 to 2 months. Forecast bias, correlation, and skills for all variables vary with season and location but are generally unsystematic and relatively constant with forecast lead time. Making it possible to meet farmers’ needs at their most preferred lead time of 1 month before the farming season. ECMWF-S4 exhibited skill in Prcp, Tmin, and Tmax in northern Ghana except for a few grid cells in MAM for Prcp and SON for Tmin and Tmax. Tmin and Tmax forecasts were more skillful than Prcp. We conclude that the participatory coproduction approach used in this study provides better insight for understanding demand-driven climate information services and that the ECMWF-S4 seasonal forecast system has the potential to provide actionable hydroclimatic information that may support farmers’ decisions.
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Macpherson, S. R., G. Deblonde, J. M. Aparicio, and B. Casati. "Impact of NOAA Ground-Based GPS Observations on the Canadian Regional Analysis and Forecast System." Monthly Weather Review 136, no. 7 (July 1, 2008): 2727–46. http://dx.doi.org/10.1175/2007mwr2263.1.
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Abstract Half-hourly GPS zenith tropospheric delay (ZTD) and collocated surface weather observations of pressure, temperature, and relative humidity are available in near–real time from the NOAA Global Systems Division (GSD) research GPS receiver network. These observations, located primarily over the continental United States, are assimilated in a research version of the Environment Canada (EC) regional (North America) analysis and forecast system. The impact of the assimilation on regional analyses and 0–48-h forecasts is evaluated for two periods: summer 2004 and winter 2004/05. Forecasts are verified against radiosonde, rain gauge, and NOAA GPS network observations. The impacts of GPS ZTD and collocated surface weather observations for the summer period are generally positive, and include reductions in forecast errors for precipitable water, surface pressure, and geopotential height. It is shown that the ZTD data are primarily responsible for these forecast error reductions. The impact on precipitation forecasts is mixed, but more positive than negative, especially for the central U.S. region and for forecasts of larger precipitation amounts. Assimilation of the collocated surface weather data with ZTD contributes to the positive impact on precipitation forecasts for the central U.S. region. The NOAA GPS network data also have a positive impact on tropical storm system forecasts over the southeast United States, in terms of both storm track and precipitation. Impacts for the winter case are generally smaller because of the lower precipitable water (PW) over North America, but some positive impacts are observed for precipitation forecasts. The greatest regional impacts in the winter case are observed for the southeast U.S. (the Gulf) region where average PW is highest.
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Reyes-García, Victoria, Álvaro Fernández-Llamazares, Maximilien Guèze, and Sandrine Gallois. "Does Weather Forecasting Relate to Foraging Productivity? An Empirical Test among Three Hunter-Gatherer Societies." Weather, Climate, and Society 10, no. 1 (January 1, 2018): 163–77. http://dx.doi.org/10.1175/wcas-d-17-0064.1.
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Abstract Previous research has studied the association between ethnoclimatological knowledge and decision-making in agriculture and pastoral activities but has paid scant attention to how ethnoclimatological knowledge might affect hunting and gathering, an important economic activity for many rural populations. The work presented here tests whether people who can forecast temperature and rain display higher hunting and gathering returns (measured as kilograms per hour for hunting and cash equivalent for gathering). Data were collected among three indigenous, small-scale, subsistence-based societies largely dependent on hunting and gathering for their livelihoods: the Tsimane’ (Amazonia, n = 107), the Baka (Congo basin, n = 164), and the Punan Tubu (Borneo, n = 103).The ability to forecast rainfall and temperature varied from one society to another, but the average consistency between people’s 1-day rainfall and temperature forecasts and instrumental measurements was low. This study found a statistically significant positive association between consistency in forecasting rain and the probability that a person engaged in hunting. Conversely, neither consistency in forecasting rain nor consistency in forecasting temperature were associated in a statistically significant way with actual returns to hunting or gathering activities. The authors discuss methodological limitations of the approach, suggesting improvements for future work. This study concludes that, other than methodological issues, the lack of strong associations might be partly explained by the fact that an important characteristic of local knowledge systems, including ethnoclimatological knowledge, is that they are widely socialized and shared.
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Han, Ji-Young, Song-You Hong, Kyo-Sun Sunny Lim, and Jongil Han. "Sensitivity of a Cumulus Parameterization Scheme to Precipitation Production Representation and Its Impact on a Heavy Rain Event over Korea." Monthly Weather Review 144, no. 6 (May 13, 2016): 2125–35. http://dx.doi.org/10.1175/mwr-d-15-0255.1.
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Abstract The sensitivity of a cumulus parameterization scheme (CPS) to a representation of precipitation production is examined. To do this, the parameter that determines the fraction of cloud condensate converted to precipitation in the simplified Arakawa–Schubert (SAS) convection scheme is modified following the results from a cloud-resolving simulation. While the original conversion parameter is assumed to be constant, the revised parameter includes a temperature dependency above the freezing level, which leads to less production of frozen precipitating condensate with height. The revised CPS has been evaluated for a heavy rainfall event over Korea as well as medium-range forecasts using the Global/Regional Integrated Model system (GRIMs). The inefficient conversion of cloud condensate to convective precipitation at colder temperatures generally leads to a decrease in precipitation, especially in the category of heavy rainfall. The resultant increase of detrained moisture induces moistening and cooling at the top of clouds. A statistical evaluation of the medium-range forecasts with the revised precipitation conversion parameter shows an overall improvement of the forecast skill in precipitation and large-scale fields, indicating importance of more realistic representation of microphysical processes in CPSs.
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Sanders, Kristopher J., and Brian L. Barjenbruch. "Analysis of Ice-to-Liquid Ratios during Freezing Rain and the Development of an Ice Accumulation Model." Weather and Forecasting 31, no. 4 (June 27, 2016): 1041–60. http://dx.doi.org/10.1175/waf-d-15-0118.1.
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Abstract Substantial freezing rain or drizzle occurs in about 24% of winter weather events in the continental United States. Proper preparation for these freezing rain events requires accurate forecasts of ice accumulation on various surfaces. The Automated Surface Observing System (ASOS) has become the primary surface weather observation system in the United States, and more than 650 ASOS sites have implemented an icing sensor as of March 2015. ASOS observations that included ice accumulation were examined from January 2013 through February 2015. The data chosen for this study consist of 60-min periods of continuous freezing rain with precipitation rates ≥ 0.5 mm h−1 (0.02 in. h−1) and greater than a trace of ice accumulation, yielding a dataset of 1255 h of observations. Ice:liquid ratios (ILRs) were calculated for each 60-min period and analyzed with 60-min mean values of temperature, wet-bulb temperature, wind speed, and precipitation rate. The median ILR for elevated horizontal (radial) ice accumulation was 0.72:1 (0.28:1), with a 25th percentile of 0.50:1 (0.20:1) and a 75th percentile of 1.0:1 (0.40:1). Strong relationships were identified between ILR and precipitation rate, wind speed, and wet-bulb temperature. The results were used to develop a multivariable Freezing Rain Accumulation Model (FRAM) for use in predicting ice accumulation incorporating these commonly forecast variables as input. FRAM performed significantly better than other commonly used forecast methods when tested on 20 randomly chosen icing events, with a mean absolute error (MAE) of 1.17 mm (0.046 in.), and a bias of −0.03 mm (−0.001 in.).
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Neiman, Paul J., Daniel J. Gottas, Allen B. White, Lawrence J. Schick, and F. Martin Ralph. "The Use of Snow-Level Observations Derived from Vertically Profiling Radars to Assess Hydrometeorological Characteristics and Forecasts over Washington’s Green River Basin." Journal of Hydrometeorology 15, no. 6 (December 1, 2014): 2522–41. http://dx.doi.org/10.1175/jhm-d-14-0019.1.
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Abstract Two vertically pointing S-band radars (coastal and inland) were operated in western Washington during two winters to monitor brightband snow-level altitudes. Similar snow-level characteristics existed at both sites, although the inland site exhibited lower snow levels by ~70 m because of proximity to cold continental air, and snow-level altitude changes were delayed there by several hours owing to onshore translation of weather systems. The largest precipitation accumulations and rates occurred when the snow level was largely higher than the adjacent terrain. A comparison of these observations with long-term operational radiosonde data reveals that the radar snow levels mirrored climatological conditions. The inland radar data were used to assess the performance of nearby operational freezing-level forecasts. The forecasts possessed a lower-than-observed bias of 100–250 m because of a combination of forecast error and imperfect representativeness between the forecast and observing points. These forecast discrepancies increased in magnitude with higher observed freezing levels, thus representing the hydrologically impactful situations where a greater fraction of mountain basins receive rain rather than snow and generate more runoff than anticipated. Vertical directional wind shear calculations derived from wind-profiler data, and concurrent surface temperature data, reveal that most snow-level forecast discrepancies occurred with warm advection aloft and low-level cold advection through the Stampede Gap. With warm advection, forecasts were too high (low) for observed snow levels below (above) 1.25 km MSL. An analysis of sea level pressure differences across the Cascades indicated that mean forecasts were too high (low) for observed snow levels below (above) 1.25 km MSL when higher pressure was west (east) of the range.
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Evin, Guillaume, Matthieu Lafaysse, Maxime Taillardat, and Michaël Zamo. "Calibrated ensemble forecasts of the height of new snow using quantile regression forests and ensemble model output statistics." Nonlinear Processes in Geophysics 28, no. 3 (September 16, 2021): 467–80. http://dx.doi.org/10.5194/npg-28-467-2021.
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Abstract. Height of new snow (HN) forecasts help to prevent critical failures of infrastructures in mountain areas, e.g. transport networks and ski resorts. The French national meteorological service, Météo-France, operates a probabilistic forecasting system based on ensemble meteorological forecasts and a detailed snowpack model to provide ensembles of HN forecasts. These forecasts are, however, biased and underdispersed. As for many weather variables, post-processing methods can be used to alleviate these drawbacks and obtain meaningful 1 to 4 d HN forecasts. In this paper, we compare the skill of two post-processing methods. The first approach is an ensemble model output statistics (EMOS) method, which can be described as a nonhomogeneous regression with a censored shifted Gamma distribution. The second approach is based on quantile regression forests, using different meteorological and snow predictors. Both approaches are evaluated using a 22 year reforecast. Thanks to a larger number of predictors, the quantile regression forest is shown to be a powerful alternative to EMOS for the post-processing of HN ensemble forecasts. The gain of performance is large in all situations but is particularly marked when raw forecasts completely miss the snow event. This type of situation happens when the rain–snow transition elevation is overestimated by the raw forecasts (rain instead of snow in the raw forecasts) or when there is no precipitation in the forecast. In that case, quantile regression forests improve the predictions using the other weather predictors (wind, temperature, and specific humidity).
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Müller, Omar V., Miguel A. Lovino, and Ernesto H. Berbery. "Evaluation of WRF Model Forecasts and Their Use for Hydroclimate Monitoring over Southern South America." Weather and Forecasting 31, no. 3 (June 1, 2016): 1001–17. http://dx.doi.org/10.1175/waf-d-15-0130.1.
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Abstract Weather forecasting and monitoring systems based on regional models are becoming increasingly relevant for decision support in agriculture and water management. This work evaluates the predictive and monitoring capabilities of a system based on WRF Model simulations at 15-km grid spacing over the La Plata basin (LPB) in southern South America, where agriculture and water resources are essential. The model’s skill up to a lead time of 7 days is evaluated with daily precipitation and 2-m temperature in situ observations for the 2-yr period from 1 August 2012 to 31 July 2014. Results show high prediction performance with 7-day lead time throughout the domain and particularly over LPB, where about 70% of rain and no-rain days are correctly predicted. Also, the probability of detection of rain days is above 80% in humid regions. Temperature observations and forecasts are highly correlated (r > 0.80) while mean absolute errors, even at the maximum lead time, remain below 2.7°C for minimum and mean temperatures and below 3.7°C for maximum temperatures. The usefulness of WRF products for hydroclimate monitoring was tested for an unprecedented drought in southern Brazil and for a slightly above normal precipitation season in northeastern Argentina. In both cases the model products reproduce the observed precipitation conditions with consistent impacts on soil moisture, evapotranspiration, and runoff. This evaluation validates the model’s usefulness for forecasting weather up to 1 week in advance and for monitoring climate conditions in real time. The scores suggest that the forecast lead time can be extended into a second week, while bias correction methods can reduce some of the systematic errors.
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Fan, Xingang, and Jeffrey S. Tilley. "Dynamic Assimilation of MODIS-Retrieved Humidity Profiles within a Regional Model for High-Latitude Forecast Applications." Monthly Weather Review 133, no. 12 (December 1, 2005): 3450–80. http://dx.doi.org/10.1175/mwr3044.1.
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Abstract A “hot start” technique is applied to the fifth-generation Pennsylvania State University–National Center for Atmospheric Research (PSU–NCAR) Mesoscale Model (MM5) to dynamically assimilate cloud properties and humidity profiles retrieved from the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument on board the NASA Earth Observing System polar-orbiting satellites. The assimilation approach has been studied through extensive numerical experimentation for high-latitude rain events to demonstrate the feasibility and the benefit of the approach on the model cloud and precipitation simulation/forecast. The ingestion of MODIS-retrieved cloud and clear-air humidity information impacts MM5 cloud fields on both a microphysical and macrophysical level. From short-term (6–12 h) forecast experiments conducted for a preliminary test case and 16 extensive summer and winter experiments, the following primary conclusions have been reached. 1) It is feasible to introduce MODIS-retrieved cloud-top properties and humidity profiles into the MM5 model in a hot start mode without disrupting model stability and evolutionary continuity. 2) The introduction of high-resolution MODIS information produced more accurate humidity fields and resulted in increased mesoscale structure in the cloud and precipitation fields. 3) The opportunistic ingestion of MODIS data at its observation time into the model leads to improved 6–12-h model precipitation forecasts with respect to not only the frequency of occurrences, but also the magnitude of precipitation amounts. 4) Verification with three-dimensional analyses indicates some improvement in model forecasts of temperature, wind, pressure perturbation, and sea level pressure as well. 5) Verification with surface station observations indicates that model forecasts of 2-m temperature, 2-m relative humidity, 10-m winds, and sea level pressure are also improved, most notably for the summer cases. The largest improvement in forecast skill is for 2-m relative humidity (12%).
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Du Duc, Tien, Lars Robert Hole, Duc Tran Anh, Cuong Hoang Duc, and Thuy Nguyen Ba. "Verification of Forecast Weather Surface Variables over Vietnam Using the National Numerical Weather Prediction System." Advances in Meteorology 2016 (2016): 1–11. http://dx.doi.org/10.1155/2016/8152413.
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The national numerical weather prediction system of Vietnam is presented and evaluated. The system is based on three main models, namely, the Japanese Global Spectral Model, the US Global Forecast System, and the US Weather Research and Forecasting (WRF) model. The global forecast products have been received at 0.25- and 0.5-degree horizontal resolution, respectively, and the WRF model has been run locally with 16 km horizontal resolution at the National Center for Hydro-Meteorological Forecasting using lateral conditions from GSM and GFS. The model performance is evaluated by comparing model output against observations of precipitation, wind speed, and temperature at 168 weather stations, with daily data from 2010 to 2014. In general, the global models provide more accurate forecasts than the regional models, probably due to the low horizontal resolution in the regional model. Also, the model performance is poorer for stations with altitudes greater than 500 meters above sea level (masl). For tropical cyclone performance validations, the maximum wind surface forecast from global and regional models is also verified against the best track of Joint Typhoon Warning Center. Finally, the model forecast skill during a recent extreme rain event in northeast Vietnam is evaluated.
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Ikeda, Kyoko, Matthias Steiner, and Gregory Thompson. "Examination of Mixed-Phase Precipitation Forecasts from the High-Resolution Rapid Refresh Model Using Surface Observations and Sounding Data." Weather and Forecasting 32, no. 3 (April 17, 2017): 949–67. http://dx.doi.org/10.1175/waf-d-16-0171.1.
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Abstract Accurate prediction of mixed-phase precipitation remains challenging for numerical weather prediction models even at high resolution and with a sophisticated explicit microphysics scheme and diagnostic algorithm to designate the surface precipitation type. Since mixed-phase winter weather precipitation can damage infrastructure and produce significant disruptions to air and road travel, incorrect surface precipitation phase forecasts can have major consequences for local and statewide decision-makers as well as the general public. Building upon earlier work, this study examines the High-Resolution Rapid Refresh (HRRR) model’s ability to forecast the surface precipitation phase, with a particular focus on model-predicted vertical temperature profiles associated with mixed-phase precipitation, using upper-air sounding observations as well as the Automated Surface Observing Systems (ASOS) and Meteorological Phenomena Identification Near the Ground (mPING) observations. The analyses concentrate on regions of mixed-phase precipitation from two winter season events. The results show that when both the observational and model data indicated mixed-phase precipitation at the surface, the model represents the observed temperature profile well. Overall, cases where the model predicted rain but the observations indicated mixed-phase precipitation generally show a model surface temperature bias of <2°C and a vertical temperature profile similar to the sounding observations. However, the surface temperature bias was ~4°C in weather systems involving cold-air damming in the eastern United States, resulting in an incorrect surface precipitation phase or the duration (areal coverage) of freezing rain being much shorter (smaller) than the observation. Cases with predicted snow in regions of observed mixed-phase precipitation present subtle difference in the elevated layer with temperatures near 0°C and the near-surface layer.
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Lopez, Philippe, and Peter Bauer. "“1D+4DVAR” Assimilation of NCEP Stage-IV Radar and Gauge Hourly Precipitation Data at ECMWF." Monthly Weather Review 135, no. 7 (July 1, 2007): 2506–24. http://dx.doi.org/10.1175/mwr3409.1.
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Abstract The one- plus four-dimensional variational data assimilation (“1D+4DVAR”) method currently run in operations at ECMWF with rain-affected radiances from the Special Sensor Microwave Imager is used to study the potential impact of assimilating NCEP stage-IV analyses of hourly accumulated surface precipitation over the U.S. mainland. These data are a combination of rain gauge measurements and observations from the high-resolution Doppler Next-Generation Weather Radars. Several 1D+4DVAR experiments have been run over a month in spring 2005. First, the quality of the precipitation forecasts in the control experiment is assessed. Then, it is shown that the impact of the assimilation of the additional rain observations on global scores of dynamical fields and temperature is rather neutral, while precipitation scores are improved for forecast ranges up to 12 h. Additional 1D+4DVAR experiments in which all moisture-affected observations are removed over the United States demonstrate that the NCEP stage-IV precipitation data on their own can clearly be beneficial to the analyses and subsequent forecasts of the moisture field. This result suggests that the potential impact of precipitation observations is overshadowed by the influence of other high-quality humidity observations, in particular, radiosondes. It also confirms that the assimilation of precipitation observations has the ability to improve the quality of moisture analyses and forecasts in data-sparse regions. Finally, the limitations inherent in the current assimilation of precipitation data, their implications for the future, and possible ways of improvement are discussed.
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Wilkinson, Sara, Marc Carmichael, and Richardo Khonasty. "Towards smart green wall maintenance and Wallbot technology." Property Management 39, no. 4 (March 12, 2021): 466–78. http://dx.doi.org/10.1108/pm-09-2020-0062.
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PurposeThe UN forecast of a 3-degree Celsius global temperature increase by 2,100 will exacerbate excessive heat. Population growth, urban densification, climate change and global warming contribute to heat waves, which are more intense in high-density environments. With urbanisation, vegetation is replaced by impervious materials which contribute to the urban heat island effect. Concurrently, adverse health outcomes and heat- related deaths are increasing, and heat stress affects labour productivity. More green infrastructure, such as green walls, is needed to mitigate these effects; however maintenance costs, OH&S issues and perceptions of fire risk inhibit take up. What if these barriers could be overcome by a green Wallbot? This research examines the feasibility of integrating smart technology in the form of a Wallbot.Design/methodology/approachThe research design comprised two workshops with key stakeholders; comprising green wall designers and installers, green wall maintenance teams, project managers and building owners with green wall installations, horticulture scientists, designers and mechatronics engineers. The aim was to gain a deeper understanding of the issues affecting maintenance of green walls on different building types in New South Wales Australia to inform the design of a prototype robot to maintain green walls.FindingsThe Wallbot has great potential to overcome the perceived barriers associated with maintaining green walls and also fire risk and detection. If these barriers are addressed, other locations, such as the sides of motorways or rail corridors, could be used for more green wall installations thereby increasing mitigation of UHI. This innovation would be a welcome addition to smart building technology and property maintenance.Research limitations/implicationsThis is a pilot study, and the sample of stakeholders attending the workshops was small, though experienced. The range of green walls is varied, and it was decided to focus initially on a specific type of green wall design for the prototype Wallbot. Therefore other types and sizes of green walls may suit other specifications of Wallbot design.Practical implicationsTo date, no robot exists that maintains green walls, and this innovative research developed a prototype for trialling maintenance and inspection.Originality/valueTo date, no robot exists that maintains green walls. No study to date has assessed stakeholder perceptions and developed prototype Wallbot technology.
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Koster, R. D., S. P. P. Mahanama, T. J. Yamada, Gianpaolo Balsamo, A. A. Berg, M. Boisserie, P. A. Dirmeyer, et al. "The Second Phase of the Global Land–Atmosphere Coupling Experiment: Soil Moisture Contributions to Subseasonal Forecast Skill." Journal of Hydrometeorology 12, no. 5 (October 1, 2011): 805–22. http://dx.doi.org/10.1175/2011jhm1365.1.
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Abstract The second phase of the Global Land–Atmosphere Coupling Experiment (GLACE-2) is a multi-institutional numerical modeling experiment focused on quantifying, for boreal summer, the subseasonal (out to two months) forecast skill for precipitation and air temperature that can be derived from the realistic initialization of land surface states, notably soil moisture. An overview of the experiment and model behavior at the global scale is described here, along with a determination and characterization of multimodel “consensus” skill. The models show modest but significant skill in predicting air temperatures, especially where the rain gauge network is dense. Given that precipitation is the chief driver of soil moisture, and thereby assuming that rain gauge density is a reasonable proxy for the adequacy of the observational network contributing to soil moisture initialization, this result indeed highlights the potential contribution of enhanced observations to prediction. Land-derived precipitation forecast skill is much weaker than that for air temperature. The skill for predicting air temperature, and to some extent precipitation, increases with the magnitude of the initial soil moisture anomaly. GLACE-2 results are examined further to provide insight into the asymmetric impacts of wet and dry soil moisture initialization on skill.
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Simanjuntak, Febryanto, Ilham Jamaluddin, Tang-Huang Lin, Hary Aprianto Wijaya Siahaan, and Ying-Nong Chen. "Rainfall Forecast Using Machine Learning with High Spatiotemporal Satellite Imagery Every 10 Minutes." Remote Sensing 14, no. 23 (November 24, 2022): 5950. http://dx.doi.org/10.3390/rs14235950.
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Increasing the accuracy of rainfall forecasts is crucial as an effort to prevent hydrometeorological disasters. Weather changes that can occur suddenly and in a local scope make fast and precise weather forecasts increasingly difficult to inform. Additionally, the results of the numerical weather model used by the Indonesia Agency for Meteorology, Climatology, and Geophysics are only able to predict the rainfall with a temporal resolution of 1–3 h and cannot yet address the need for rainfall information with high spatial and temporal resolution. Therefore, this study aims to provide the rainfall forecast in high spatiotemporal resolution using Himawari-8 and GPM IMERG (Global Precipitation Measurement: The Integrated Multi-satellite Retrievals) data. The multivariate LSTM (long short-term memory) forecasting is employed to predict the cloud brightness temperature by using the selected Himawari-8 bands as the input and training data. For the rain rate regression, we used the random forest technique to identify the rainfall and non-rainfall pixels from GPM IMERG data as the input in advance. The results of the rainfall forecast showed low values of mean error and root mean square error of 0.71 and 1.54 mm/3 h, respectively, compared to the observation data, indicating that the proposed study may help meteorological stations provide the weather information for aviation purposes.
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Yang, Lu, Mingxuan Chen, Xiaoli Wang, Linye Song, Meilin Yang, Rui Qin, Conglan Cheng, and Siteng Li. "Classification of Precipitation Type in North China Using Model-Based Explicit Fields of Hydrometeors with Modified Thermodynamic Conditions." Weather and Forecasting 36, no. 1 (February 2021): 91–107. http://dx.doi.org/10.1175/waf-d-20-0005.1.
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AbstractThe ability to forecast thermodynamic conditions aloft and near the surface is critical to the accurate forecasting of precipitation type at the surface. This paper presents an experimental version of a new scheme for diagnosing precipitation type. The method considers the optimum surface temperature threshold associated with each precipitation type and combines model-based explicit fields of hydrometeors with higher-resolution modified thermodynamic and topographic information to determine precipitation types in North China. Based on over 60 years of precipitation-type samples from North China, this study explores the climatological characteristics of the five precipitation types—snow, rain, ice pellets (IP), rain/snow mix (R/S MIX), and freezing rain (FZ)—as well as the suitable air temperature Ta and wet-bulb temperature Tw thresholds for distinguishing different precipitation types. Direct output from numerical weather prediction (NWP) models, such as temperature and humidity, was modified by downscaling and bias correction, as well as by incorporating the latest surface observational data and high-resolution topographic data. Validation of the precipitation-type forecasts from this scheme was performed against observations from the 2016 to 2019 winter seasons and two case studies were also analyzed. Compared with the similar diagnostic routine in the High-Resolution Rapid Refresh (HRRR) forecasting system used to predict precipitation type over North China, the skill of the method proposed here is similar for rain and better for snow, R/S MIX, and FZ. Furthermore, depiction of the diagnosed boundary between R/S MIX and snow is good in most areas. However, the number of misclassifications for R/S MIX is significantly larger than for rain and snow.
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Rong, Haina, and Francisco de León. "Load Estimation of Complex Power Networks from Transformer Measurements and Forecasted Loads." Complexity 2020 (January 22, 2020): 1–14. http://dx.doi.org/10.1155/2020/2941809.
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This paper presents a load estimation method applicable to complex power networks (namely, heavily meshed secondary networks) based on available network transformer measurements. The method consists of three steps: network reduction, load forecasting, and state estimation. The network is first mathematically reduced to the terminals of loads and measurement points. A load forecasting approach based on temperature is proposed to solve the network unobservability. The relationship between outdoor temperature and power consumption is studied. A power-temperature curve, a nonlinear function, is obtained to forecast loads as the temperature varies. An “effective temperature” reflecting complex weather conditions (sun irradiation, humidity, rain, etc.) is introduced to properly consider the effect on the power consumption of cooling and heating devices. State estimation is adopted to compute loads using network transformer measurements and forecasted loads. Experiments conducted on a real secondary network in New York City with 1040 buses verify the effectiveness of the proposed method.
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Rössler, O., P. Froidevaux, U. Börst, R. Rickli, O. Martius, and R. Weingartner. "Retrospective analysis of a non-forecasted rain-on-snow flood in the Alps – a matter of model-limitations or unpredictable nature?" Hydrology and Earth System Sciences Discussions 10, no. 10 (October 29, 2013): 12861–904. http://dx.doi.org/10.5194/hessd-10-12861-2013.
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Abstract. On 10 October 2011, a rain-on-snow flood occurred in the Bernese Alps, Switzerland, and caused significant damage. As this flood peak was unpredicted by the flood forecast system, questions were raised concerning what has caused this flood and whether it was predictable at all. In this study, we focused on one valley that was heavily hit by the event, the Loetschen valley (160 km2), and aimed to reconstruct the anatomy of this rain-on-snow flood from the synoptic conditions represented by European Centre for Medium-Range Weather Forecasts ECWMF analysis data, and the local meteorology within the valley recorded by an extensive met-station network. In addition, we applied the hydrological model WaSiM-ETH to improve our hydrological process understanding about this event and to demonstrate the predictability of this rain-on-snow flood. We found an atmospheric river bringing moist and warm air to Switzerland that followed an anomalous cold front with sustained snowfall to be central for this rain-on-snow event. Intensive rainfall (average 100 mm day−1) was accompanied by a drastic temperature increase (+8 K) that shifted the zero degree line from 1500 m a.s.l. to 3200 m a.s.l. in 12 h. The northern flank of the valley received significantly more precipitation than the southern flank, leading to an enormous flood in tributaries along the northern flank, while the tributaries along the southern flank remained nearly unchanged. We hypothesized that the reason for this was a cavity circulation combined with a seeder-feeder-cloud system enhancing both local rainfall and snow melt by condensation of the warm, moist air on the snow. Applying and adjusting the hydrological model, we show that both the latent and the sensible heat fluxes were responsible for the flood and that locally large amounts of precipitation (up to 160 mm rainfall in 12 h) was necessary to produce the estimated flood peak. With considerable adjustments to the model and meteorological input data, we were able to reproduce the flood peak, demonstrating the ability of the model to reproduce the flood. However, driving the optimized model with COSMO-2 forecast data, we still failed to simulate the flood precisely because COSMO-2 forecast data underestimated both the local precipitation peak and the temperature increase. Thus, this rain-on-snow flood was predictable, but requires a special model set up and extensive and locally precise meteorological input data, especially in terms of both precipitation and temperature.
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Ravi, V. Venkatrama, M. Mukesh Reddy, K. Sai Teja, Ch Sai Niteesh, and Dr B. Sekhar Babu. "Weather Prediction." International Journal for Research in Applied Science and Engineering Technology 10, no. 4 (October 31, 2022): 459–62. http://dx.doi.org/10.22214/ijraset.2022.47020.
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Abstract: Weather maintains and sustains a extremely delicate balance of life on planet Earth. Climate conducts a totally critical function in many key production sectors, e.g., farming. global climate change with high charging nowadays, which is why old weather forecasts are becoming closer and fewer powerful and still be annoying. The weather is altogether one in every of 1 in every of the best natural barriers all told parts of our lives within the world, we’d like to seem at the weather including temperature, rain, humidity, etc. The aim of our paper is to effectively forecast weather. Earth's climate will change over an extended period of your time and also what quite impact it'll wear the lives of future generations. Our various day to day choices is littered with the weather and having an accurate due to forecast the weather may be a crucial process. But these predictions often don’t seem to be always faithful life. Our approach greatly enhancing the model accuracy by implement various Machine learning algorithms.
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Qian, Weihong, Ning Jiang, and Jun Du. "Anomaly-Based Weather Analysis versus Traditional Total-Field-Based Weather Analysis for Depicting Regional Heavy Rain Events." Weather and Forecasting 31, no. 1 (January 28, 2016): 71–93. http://dx.doi.org/10.1175/waf-d-15-0074.1.
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Abstract Although the use of anomaly fields in the forecast process has been shown to be useful and has caught forecasters’ attention, current short-range (1–3 days) weather analyses and forecasts are still predominantly total-field based. This paper systematically examines the pros and cons of anomaly- versus total-field-based approaches in weather analysis using a case from 1 July 1991 (showcase) and 41 cases from 1998 (statistics) of heavy rain events that occurred in China. The comparison is done for both basic atmospheric variables (height, temperature, wind, and humidity) and diagnostic parameters (divergence, vorticity, and potential vorticity). Generally, anomaly fields show a more enhanced and concentrated signal (pattern) directly related to surface anomalous weather events, while total fields can obscure the visualization of anomalous features due to the climatic background. The advantage is noticeable in basic atmospheric variables, but is marginal in nonconservative diagnostic parameters and is lost in conservative diagnostic parameters. Sometimes a mix of total and anomaly fields works the best; for example, in the moist vorticity when anomalous vorticity combines with total moisture, it can depict the heavy rain area the best when comparing to either the purely total or purely anomalous moist vorticity. Based on this study, it is recommended that anomaly-based weather analysis could be a valuable supplement to the commonly used total-field-based approach. Anomalies can help a forecaster to more quickly identify where an abnormal weather event might occur as well as more easily pinpoint possible meteorological causes than a total field. However, one should not use the anomaly structure approach alone to explain the underlying dynamics without a total field.
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Benedetti, Angela, and Marta Janisková. "Assimilation of MODIS Cloud Optical Depths in the ECMWF Model." Monthly Weather Review 136, no. 5 (May 1, 2008): 1727–46. http://dx.doi.org/10.1175/2007mwr2240.1.
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Abstract At the European Centre for Medium-Range Weather Forecasts (ECMWF), a large effort has recently been devoted to define and implement moist physics schemes for variational assimilation of rain- and cloud-affected brightness temperatures. This study expands on the current application of the new linearized moist physics schemes to assimilate cloud optical depths retrieved from the Moderate Resolution Imaging Spectroradiometer (MODIS) on board the Aqua platform for the first time in the ECMWF operational four-dimensional assimilation system. Model optical depths are functions of ice water and liquid water contents through established parameterizations. Linearized cloud schemes in turn link these cloud variables with temperature and humidity. A bias correction is applied to the optical depths to minimize the differences between model and observations. The control variables in the assimilation are temperature, humidity, winds, and surface pressure. One-month assimilation experiments for April 2006 demonstrated an impact of the assimilated MODIS cloud optical depths on the model fields, particularly temperature and humidity. Comparison with independent observations indicates a positive effect of the cloud information assimilated into the model, especially on the amount and distribution of the ice water content. The impact of the cloud assimilation on the medium-range forecast is neutral to slightly positive. Most importantly, this study demonstrates that global assimilation of cloud observations in ECMWF four-dimensional variational assimilation system (4DVAR) is technically doable but a continued research effort is necessary to achieve clear positive impacts with such data.
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Lupo, Kevin M., Ryan D. Torn, and Shu-Chih Yang. "Process-Based Evaluation of Stochastic Perturbed Microphysics Parameterization Tendencies on Ensemble Forecasts of Heavy Rainfall Events." Monthly Weather Review 150, no. 1 (January 2022): 175–91. http://dx.doi.org/10.1175/mwr-d-21-0090.1.
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Abstract Stochastic model error schemes, such as the stochastic perturbed parameterization tendencies (SPPT) and independent SPPT (iSPPT) schemes, have become an increasingly accepted method to represent model error associated with uncertain subgrid-scale processes in ensemble prediction systems (EPSs). While much of the current literature focuses on the effects of these schemes on forecast skill, this research examines the physical processes by which iSPPT perturbations to the microphysics parameterization scheme yield variability in ensemble rainfall forecasts. Members of three 120-member Weather Research and Forecasting (WRF) Model ensemble case studies, including two distinct heavy rain events over Taiwan and one over the northeastern United States, are ranked according to an area-averaged accumulated rainfall metric in order to highlight differences between high- and low-precipitation forecasts. In each case, high-precipitation members are characterized by a damping of the microphysics water vapor and temperature tendencies over the region of heaviest rainfall, while the opposite is true for low-precipitation members. Physically, the perturbations to microphysics tendencies have the greatest impact at the cloud level and act to modify precipitation efficiency. To this end, the damping of tendencies in high-precipitation forecasts suppresses both the loss of water vapor due to condensation and the corresponding latent heat release, leading to grid-scale supersaturation. Conversely, amplified tendencies in low-precipitation forecasts yield both drying and increased positive buoyancy within clouds.
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Thiaw, Wassila M., and Kingtse C. Mo. "Impact of Sea Surface Temperature and Soil Moisture on Seasonal Rainfall Prediction over the Sahel." Journal of Climate 18, no. 24 (December 15, 2005): 5330–43. http://dx.doi.org/10.1175/jcli3552.1.
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Abstract The ensemble rainfall forecasts over the Sahel for July–September (JAS) from the NCEP Coupled Forecast System (CFS) were evaluated for the period 1981–2002. The comparison with the gauge-based precipitation analysis indicates that the predicted Sahel rainfall is light and exhibits little interannual variability. The rain belt is shifted about 4° southward. One major source of rainfall errors comes from the erroneous sea surface temperature (SST) forecasts. The systematic SST error pattern has positive errors in the North Pacific and the North Atlantic and negative errors in the tropical Pacific and the southern oceans. It resembles the decadal SST mode, which has a significant influence on rainfall over the Sahel. Because the systematic SST errors were not corrected during the forecasts, persistent errors serve as an additional forcing to the atmosphere. The second source of error is from the soil moisture feedback, which contributes to the southward shift of rainfall and dryness over West Africa. This was demonstrated by the comparison between simulations (SIMs) and the Atmospheric Model Intercomparison Project (AMIP) run. Both are forced with observed SSTs. The SIMs initialized at the end of June have realistic soil moisture and do not show the southward shift of rainfall. The AMIP, which predicts soil moisture, maintains the dryness through the summer over the Sahel. For AMIP, the decreased rainfall is contributed by the decreased evaporation (E) due to the dry soil and the shift of the large temperature gradients southward. In response, the African easterly jet (AEJ) shifts southward. Since this jet is the primary source of energy for the African waves and their associated mesoscale convective systems, these too shift southward. This negative feedback contributes to increased dryness over the Sahel.
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Hodur, Richard M., and Bogumil Jakubiak. "The Effect of the Physical Parameterizations and the Land Surface on Rainfall in Poland." Weather and Forecasting 31, no. 4 (July 25, 2016): 1247–70. http://dx.doi.org/10.1175/waf-d-15-0124.1.
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Abstract High-resolution numerical experiments were conducted over two separate months to study the effect of different physical parameterizations and different representations of the land surface on the prediction of rainfall events in Poland. The Coupled Ocean–Atmosphere Mesoscale Prediction System (COAMPS) was used with 2-km grid spacing. Four sets of forecast experiments were performed. The control experiment used a slab model for the surface energy budget, a Lin-based moist physics parameterization, and a Mellor and Yamada (MY) based turbulent kinetic energy (TKE) parameterization; the Louis experiment used a version of the Louis TKE parameterization in place of MY; the LSM experiment used the Noah land surface model (LSM) in place of the slab model; and the Thompson experiment used the Thompson microphysics parameterization instead of the Lin-based microphysics. The forecasts were validated against surface and upper-air observations, as well as radar reflectivity. The Louis parameterization yielded improvements to the total rainfall and small improvements to the near-surface temperature and moisture. The Noah LSM yielded the largest improvements to the prediction of near-surface temperature and moisture, and while it led to correctly forecasting an increase in precipitation in one month, it erroneously predicted a decrease in precipitation in the other month. The Thompson microphysics produced the most skillful precipitation forecasts for late spring, but produced precipitation forecasts that were less skillful than the control experiment for early fall. The use of higher horizontal resolution (0.5 km) for two rain events led to the overprediction of rainfall, but suggested a better distribution of the rainfall.
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Kwon, Eun-Han, Byung-Ju Sohn, Dong-Eon Chang, Myoung-Hwan Ahn, and Song Yang. "Use of Numerical Forecasts for Improving TMI Rain Retrievals over the Mountainous Area in Korea." Journal of Applied Meteorology and Climatology 47, no. 7 (July 1, 2008): 1995–2007. http://dx.doi.org/10.1175/2007jamc1857.1.
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Abstract Topographical influences on the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) rain retrievals over the terrain area of the Korean peninsula were examined using a training dataset constructed from numerical mesoscale model simulations in conjunction with radiative transfer calculations. By relating numerical model outputs to rain retrievals from simulated brightness temperatures, a positive relationship between topographically forced vertical motion and rain retrievals in the upstream region over the mountainous area was found. Based on the relationship obtained, three topographical correction methods were developed by incorporating slope-forced vertical motion and its associated upward vapor flux, and vapor flux convergence in the surface boundary layer into a scattering-based TMI rain retrieval algorithm. The developed correction methods were then applied for the rain retrievals from simulated TMI brightness temperatures with model outputs and measured TMI brightness temperatures. Results showed that orographic influences on the rain formation can be included in the TMI rainfall algorithms, which tend to underestimate rainfall over the complex terrain area. It was shown that topographical corrections surely improve the rain retrieval when a strong rain event is present over the upslope region. Among various elements, moisture convergence in the boundary layer appears to be an important factor needed in the topographical correction. Overall topography-corrected estimates of rainfall showed a better agreement with ground measurements than those without correction, suggesting that satellite rain retrieval over the terrain area can be improved when accurate numerical forecast outputs are incorporated into the rain retrieval algorithm.
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Gajinov, Spasenija, Tomo Popović, Dejan Drajić, Nenad Gligorić, and Srdjan Krčo. "Qualitative parameter analysis for Botrytis cinerea forecast modelling using IoT sensor networks." Journal of Networking and Network Applications 2, no. 3 (2022): 129–35. http://dx.doi.org/10.33969/j-nana.2022.020305.
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This paper provides results of an evaluation of a fungal disease Botrytis cinerea forecast model (Model for Botrytis cinerea appearing) in vineyards for qualitative analysis of parameters which affect the development of the disease by using data from a network of connected sensors (air temperature and relative humidity, rain precipitation, and leaf wetness). The fungal disease model used by agronomists was digitalized and integrated into agroNET, a decision support tool, helping farmers to decide when to apply chemical treatments and which chemicals to use, to ensure the best growing conditions and suppress the growth of Botrytis cinerea. The temperature and humidity contexts are used to detect the risk of the disease occurrence. In this study, the impact of the humidity conditions (relative humidity, rain precipitation, and leaf wetness) is evaluated by assessing how different humidity parameters correlate with the accuracy of the Botrytis cinerea fungi forecast. Each observed parameter has its own threshold that triggers the second step of the disease modelling-risk index based on the temperature. The research showed that for relative humidity, rain precipitation, and leaf wetness measurements, a low-cost relative humidity sensor can detect, on average, 14.61% of cases, a leaf wetness sensor an additional 3.99% of risk cases, and finally, a precipitation sensor can detect an additional 0.59% of risk cases (in observed period the risk was detected in 19.19% (14.61%+3.99%0.59%) of the time), which gives a guide to farmers how to consider cost effective implementation of sensors to achieve good performance. The use of the proposed model reduced the use of pesticides up to 20%.
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Birk, Kevin, Eric Lenning, Kevin Donofrio, and Matthew T. Friedlein. "A Revised Bourgouin Precipitation-Type Algorithm." Weather and Forecasting 36, no. 2 (April 2021): 425–38. http://dx.doi.org/10.1175/waf-d-20-0118.1.
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AbstractUsing vertical temperature profiles obtained from upper-air observations or numerical weather prediction models, the Bourgouin technique calculates areas of positive melting energy and negative refreezing energy for determining precipitation type. Energies are proportional to the product of the mean temperature of a layer and its depth. Layers warmer than 0°C consist of positive energy; those colder than 0°C consist of negative energy. Sufficient melting or freezing energy in a layer can produce a phase change in a falling hydrometeor. The Bourgouin technique utilizes these energies to determine the likelihood of rain (RA) versus snow (SN) given a surface-based melting layer and ice pellets (PL) versus freezing rain (FZRA) or RA given an elevated melting layer. The Bourgouin approach was developed from a relatively small dataset but has been widely utilized by operational forecasters and in postprocessing of NWP output. Recent analysis with a larger dataset suggests ways to improve the original technique, especially when discriminating PL from FZRA or RA. This and several other issues are addressed by a modified version of the Bourgouin technique described in this article. Additional enhancements include use of the wet-bulb profile rather than temperature, a check for heterogeneous ice nucleation, and output that includes probabilities of four different weather types (RA, SN, FZRA, PL) rather than the single most likely type. Together these revisions result in improved performance and provide a more viable and valuable tool for precipitation-type forecasts. Several National Weather Service forecast offices have successfully utilized the revised tool in recent winters.
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Kimambo, Offoro Neema, Hector Chikoore, and Jabulani Ray Gumbo. "Understanding the Effects of Changing Weather: A Case of Flash Flood in Morogoro on January 11, 2018." Advances in Meteorology 2019 (April 21, 2019): 1–11. http://dx.doi.org/10.1155/2019/8505903.
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Floods are the leading cause of hydrometeorological disasters in East Africa. Regardless of where, when, and how the event has happened, floods affect social security as well as environmental damages. Understanding floods dynamics, their impacts, and management is thus critical, especially in climate risk assessment. In the present study, a flash flood (a case of an episodic hydrological event) which happened on January 11, 2018, in Morogoro, Tanzania, is examined and synthesized. Data were courtesy of the National Oceanic and Atmospheric Administration Global Forecasting System (NOAA GFS) (forecast data), Tanzania Meteorological Agency (TMA), and Sokoine University of Agriculture (for the automatic weather data). With the help of ZyGRIB-grib file visualization software (version 8.01, under General Public License (GNU GPL v3)), the forecast data and patterns of the observation from the automatic weather station (temperatures, wind speed and directions, rainfall, humidity, and pressure) and the long-term rainfall data analysis in the study area made it possible. This study contributes to the knowledge of understanding the changing weather for planning and management purposes. Both forecasts and the observations captured the flash flood event. The rain was in the category of heavy rainfall (more than 50 mm per day) as per the regional guidelines. The synergy between the forecasts and the 30-minute weather observation interval captured the fundamental weather patterns that describe the event. For studying the nature and impacts of flash floods in the region, the integration of automatic weather observation into the systems of national meteorological centers is inevitable. Additionally, as part of an integrated disaster risk reduction effort, there is a need for a review on catchment management strategies.
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Buchmann, Julio, Lawrence E. Buja, Jan Paegle, and Robert E. Dickinson. "Experiments on the effect of tropical Atlantic heating anomalies upon GCM rain forecasts over the Americas." Anuário do Instituto de Geociências 13 (December 1, 1990): 57–67. http://dx.doi.org/10.11137/1990_0_57-67.
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A series of real data experiments is performed with a general circulation model in order to ascertain the sensitivity of extended range rain forecasts over the Americas to the structure and magnitude of tropical heating anomalies. The emphasis is upon heat inputs over the tropical Atlantic which have shown particularly significant drying influences over North America in our prior simulations. The heating imposed in the prior experiments is compared to the condensation heating rates that naturally occur in the forecast model, and shown to be excessive by approximately a factor of two. Present experiments reduce the imposed anomaly by a factor of three, and also incorporate sea-surface temperature decreases over the eastern tropical Pacific Ocean. The new experimental results are in many ways consistent with our prior results. The dry North American response is statistically more significant than the South American response, and occurs at least as frequently in the different members of the experimental ensembles as in our prior experiments. The drying effect is accentuated by the presence of East Pacific cooling, but this does not appear to be the dominant influence. Over tropical South America, the Pacific and Atlantic modifications produce compensating influences, with the former dominating dominant, and allowing increased rainfall over the Amazon Basin.
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Chen, Tsing-Chang, Jenq-Dar Tsay, and Eugene S. Takle. "A Forecast Advisory for Afternoon Thunderstorm Occurrence in the Taipei Basin during Summer Developed from Diagnostic Analysis*." Weather and Forecasting 31, no. 2 (March 15, 2016): 531–52. http://dx.doi.org/10.1175/waf-d-15-0082.1.
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Abstract Summer is a dry season in northern Taiwan. By contrast, the Taipei basin, located in this region, has its maximum rainfall during summer (15 June–31 August), when 78% of this rainfall is contributed by afternoon thunderstorms. This thunderstorm activity occurs during only 20 days in summer. Because of the pronounced impacts on the well-being of three million people in the basin and the relative infrequency of occurrence, forecasting thunderstorm events is an important operational issue in the Taipei basin. The basin’s small size (30 km × 60 km), with two river exits and limited thunderstorm occurrence days, makes the development of a thunderstorm activity forecast model for this basin a great challenge. Synoptic analysis reveals a thunderstorm day may develop from morning synoptic conditions free of clouds/rain, with a NW–SE-oriented dipole located south of Taiwan and southwesterlies straddling the low and high of this dipole. The surface meteorological conditions along the two river valleys exhibit distinct diurnal variations of pressure, temperature, dewpoint depression, relative humidity, and land–sea breezes. The primary features of the synoptic conditions and timings of the diurnal cycles for the four surface variables are utilized to develop a two-step hybrid forecast advisory for thunderstorm occurrence. Step 1 validates the 24-h forecasts for the 0000 UTC (0800 LST) synoptic conditions and timings for diurnal variations for the first five surface variables on thunderstorm days. Step 2 validates the same synoptic and surface meteorological conditions (including sea-breeze onset time) observed on the thunderstorm day. The feasibility of the proposed forecast advisory is successfully demonstrated by these validations.
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Baker, L. H., A. C. Rudd, S. Migliorini, and R. N. Bannister. "Representation of model error in a convective-scale ensemble prediction system." Nonlinear Processes in Geophysics 21, no. 1 (January 8, 2014): 19–39. http://dx.doi.org/10.5194/npg-21-19-2014.
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Abstract. In this paper ensembles of forecasts (of up to six hours) are studied from a convection-permitting model with a representation of model error due to unresolved processes. The ensemble prediction system (EPS) used is an experimental convection-permitting version of the UK Met Office's 24-member Global and Regional Ensemble Prediction System (MOGREPS). The method of representing model error variability, which perturbs parameters within the model's parameterisation schemes, has been modified and we investigate the impact of applying this scheme in different ways. These are: a control ensemble where all ensemble members have the same parameter values; an ensemble where the parameters are different between members, but fixed in time; and ensembles where the parameters are updated randomly every 30 or 60 min. The choice of parameters and their ranges of variability have been determined from expert opinion and parameter sensitivity tests. A case of frontal rain over the southern UK has been chosen, which has a multi-banded rainfall structure. The consequences of including model error variability in the case studied are mixed and are summarised as follows. The multiple banding, evident in the radar, is not captured for any single member. However, the single band is positioned in some members where a secondary band is present in the radar. This is found for all ensembles studied. Adding model error variability with fixed parameters in time does increase the ensemble spread for near-surface variables like wind and temperature, but can actually decrease the spread of the rainfall. Perturbing the parameters periodically throughout the forecast does not further increase the spread and exhibits "jumpiness" in the spread at times when the parameters are perturbed. Adding model error variability gives an improvement in forecast skill after the first 2–3 h of the forecast for near-surface temperature and relative humidity. For precipitation skill scores, adding model error variability has the effect of improving the skill in the first 1–2 h of the forecast, but then of reducing the skill after that. Complementary experiments were performed where the only difference between members was the set of parameter values (i.e. no initial condition variability). The resulting spread was found to be significantly less than the spread from initial condition variability alone.
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Williamson, David L., and Jerry G. Olson. "A Comparison of Forecast Errors in CAM2 and CAM3 at the ARM Southern Great Plains Site." Journal of Climate 20, no. 18 (September 15, 2007): 4572–85. http://dx.doi.org/10.1175/jcli4267.1.
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Abstract The authors compare short forecast errors and the balance of terms in the moisture and temperature prediction equations that lead to those errors for the Community Atmosphere Model versions 2 and 3 (CAM2 and CAM3, respectively) at T42 truncation. The comparisons are made for an individual model column from global model forecasts at the Atmospheric Radiation Measurement Program (ARM) Southern Great Plains site for the April 1997 and June–July 1997 intensive observing periods. The goal is to provide insight into parameterization errors in the CAM, which ultimately should lead to improvements in the way processes are modeled. The atmospheric initial conditions are obtained from the 40-yr ECMWF Re-Analysis (ERA-40). The land initial conditions are spun up to be consistent with those analyses. The differences between the model formulations that are responsible for the major differences in the forecast errors and/or parameterization behaviors are identified. A sequence of experiments is performed, accumulating the changes from CAM3 back toward CAM2 to demonstrate the effect of the differences in formulations. In June–July 1997 the CAM3 temperature and moisture forecast errors were larger than those of CAM2. The terms identified as being responsible for the differences are 1) the convective time scale assumed for the Zhang–McFarlane deep convection, 2) the energy associated with the conversion between water and ice of the rain associated with the Zhang–McFarlane convection parameterization, and 3) the dependence of the rainfall evaporation on cloud fraction. In April 1997 the CAM2 and CAM3 temperature and moisture forecast errors are very similar, but different tendencies arising from modifications to one parameterization component are compensated by responding changes in another component to yield the same total moisture tendency. The addition of detrainment of water in CAM3 by the Hack shallow convection to the prognostic cloud water scheme is balanced by a responding difference in the advective tendency. A halving of the time scale assumed for the Hack shallow convection was compensated by a responding change in the prognostic cloud water. Changes to the cloud fraction parameterization affect the radiative heating, which in turn modifies the stability of the atmospheric column and affects the convection. The resulting changes in convection tendency are balanced by responding changes in the prognostic cloud water parameterization tendency.
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Benevides, Pedro, Joao Catalao, and Giovanni Nico. "Neural Network Approach to Forecast Hourly Intense Rainfall Using GNSS Precipitable Water Vapor and Meteorological Sensors." Remote Sensing 11, no. 8 (April 23, 2019): 966. http://dx.doi.org/10.3390/rs11080966.
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This work presents a methodology for the short-term forecast of intense rainfall based on a neural network and the integration of Global Navigation and Positioning System (GNSS) and meteorological data. Precipitable water vapor (PWV) derived from GNSS is combined with surface pressure, surface temperature and relative humidity obtained continuously from a ground-based meteorological station. Five years of GNSS data from one station in Lisbon, Portugal, are processed. Data for precipitation forecast are also collected from the meteorological station. Spaceborne Spinning Enhanced Visible and Infrared Imager (SEVIRI) data of cloud top measurements are also gathered, providing collocated information on an hourly basis. In previous studies it was found that the time-varying PWV is correlated with rainfall and can be used to detected heavy rain. However, a significant number of false positives were found, meaning that the evolution of PWV does not contain enough information to infer future rain. In this work, a nonlinear autoregressive exogenous neural network model (NARX) is used to process the GNSS and meteorological data to forecast the hourly precipitation. The proposed methodology improves the detection of intense rainfall events and reduces the number of false positives, with a good classification score varying from 63% up to 72% and a false positive rate of 36% down to 21%, for the tested years in the dataset. A score of 64% for intense rain events classification with 22% false positive rate is obtained for the most recent years. The method also achieves an almost 100% hit rate for the rain vs no rain detection, with close to no false alarms.
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Sun, Cunyong, Xiangjun Shi, Huiping Yan, Qixiao Jiang, and Yuxi Zeng. "Forecasting the June Ridge Line of the Western Pacific Subtropical High with a Machine Learning Method." Atmosphere 13, no. 5 (April 21, 2022): 660. http://dx.doi.org/10.3390/atmos13050660.
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The ridge line of the western Pacific subtropical high (WPSHRL) plays an important role in determining the shift in the summer rain belt in eastern China. In this study, we developed a forecast system for the June WPSHRL index based on the latest autumn and winter sea surface temperature (SST). Considering the adverse condition created by the small observed sample size, a very simple neural network (NN) model was selected to extract the non-linear relationship between input predictors (SST) and target predictands (WPSHRL) in the forecast system. In addition, some techniques were used to deal with the small sample size, enhance the stabilization of the forecast skills, and analyze the interpretability of the forecast system. The forecast experiments showed that the linear correlation coefficient between the predictions from the forecast system and their corresponding observations was around 0.6, and about three-fifths of the observed abnormal years (the years with an obviously high or low WPSHRL index) were successfully predicted. Furthermore, sensitivity experiments showed that the forecast system is relatively stable in terms of forecast skill. The above results suggest that the forecast system would be valuable in real-life applications.
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Sultan, Benjamin, Bruno Barbier, Jeanne Fortilus, Serigne Modou Mbaye, and Grégoire Leclerc. "Estimating the Potential Economic Value of Seasonal Forecasts in West Africa: A Long-Term Ex-Ante Assessment in Senegal." Weather, Climate, and Society 2, no. 1 (January 1, 2010): 69–87. http://dx.doi.org/10.1175/2009wcas1022.1.
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Abstract Recent improvements in the capability of statistical or dynamic models to predict climate fluctuations several months in advance may be an opportunity to improve the management of climatic risk in rain-fed agriculture. The aim of this paper is to evaluate the potential benefits that seasonal climate predictions can bring to farmers in West Africa. The authors have developed an archetypal bioeconomic model of a smallholder farm in Nioro du Rip, a semiarid region of Senegal. The model is used to simulate the decisions of farmers who have access to a priori information on the quality of the next rainy season. First, the potential economic benefits of a perfect rainfall prediction scheme are evaluated, showing how these benefits are affected by forecast accuracy. Then, the potential benefits of several widely used rainfall prediction schemes are evaluated: one group of schemes based on the statistical relationship between rainfall and sea surface temperatures, and one group based on the predictions of coupled ocean–atmosphere models. The results show that forecasting a dryer than average rainy season would be the most useful to Nioro du Rip farmers if they interpret forecasts as deterministic. Indeed, because forecasts are imperfect, predicting a wetter than average rainy season exposes the farmers to a high risk of failure by favoring cash crops such as maize and peanut that are highly vulnerable to drought. On the other hand, the farmers’ response to a forecast of a dryer than average rainy season minimizes the climate risk by favoring robust crops such as millet and sorghum, which will tolerate higher rainfall in case the forecast is wrong. When either statistical or dynamic climate models are used for forecasting under the same lead time and the same 31-yr hindcast period (i.e., 1970–2000), similar skill and economic values at farm level are found. When a dryer than average rainy season is predicted, both methods yield an increase of the farmers’ income—13.8% for the statistical model and 9.6% for the bias-corrected Development of a European Multimodel Ensemble System for Seasonal-to-Interannual Prediction (DEMETER) multimodel ensemble mean.
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Jury, Mark R. "Weather–Climate Interactions in the Eastern Antilles and the 2013 Christmas Storm." Earth Interactions 18, no. 19 (November 1, 2014): 1–20. http://dx.doi.org/10.1175/ei-d-14-0011.1.
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Abstract This study considers eastern Antilles (11°–18°N, 64°–57°W) weather and climate interactions in the context of the 2013 Christmas storm. This unseasonal event caused flash flooding in Grenada, St. Vincent, St. Lucia, Martinique, and Dominica from 24 to 25 December 2013, despite having winds <15 m s−1. The meteorological scenario and short-term forecasts are analyzed. At the low level, a convective wave propagated westward while near-equatorial upper westerly winds surged with eastward passage of a trough. The combination of tropical moisture, cyclonic vorticity, and uplift resulted in rain rates greater than 30 mm h−1 and many stations reporting 200 mm. Although forecast rainfall was low and a few hours late, weather services posted flood warnings in advance. At the climate scale, the fresh Orinoco River plume brought into the region by the North Brazil Current together with solar radiation greater than 200 W m−2, enabled sea temperatures to reach 28°C, and supplied convective available potential energy greater than 1800 J kg−1. Climate change model simulations are compared with reference fields and trends are analyzed in the eastern Antilles. While temperatures are set to increase, the frequency of flood events appears to decline in the future.
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Nayak, Siva Durga Prasad, and K. A. Narayan. "Prediction of dengue outbreaks in Kerala state using disease surveillance and meteorological data." International Journal Of Community Medicine And Public Health 6, no. 10 (September 26, 2019): 4392. http://dx.doi.org/10.18203/2394-6040.ijcmph20194500.
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Background: Dengue is one of the most serious and fast emerging tropical diseases. Its incidence of is influenced by many meteorological factors such as rain fall in mm, temperature, humidity etc. Information about these factors can be used to forecast the incidence of dengue fever cases in the next coming months.Methods: The current study was an analytical study using retrospective secondary data from Kerala state. The annual integrated disease surveillance reports of dengue fever cases. Rain fall data and mean monthly temperatures for a period of twelve years from 2006 to 2017 were used. Best fitted model was developed and accuracy of the prediction was tested. All analyses were performed in R software using the mgcv package and nlme package.Results: A total of 144 months study period from January 2006 to December 2017 was used for analysis. Five different models developed for prediction of dengue cases among them, best fitted model including optimal combination of meteorological variables and recent and long term transition of dengue was selected. Out of 84 months predictions in the training period, 68 months prediction was correctly negative, 5 months prediction was correctly positive, 2 months prediction was incorrectly negative and 9 months prediction was incorrectly positive.Conclusions: A better predictive generalized additive model can be developed using the optimal combination of meteorological predictors and dengue fever counts. It will enable the health care administrators to forecast future out breaks and to take better precautionary measures.
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Ruiz, Juan J., Celeste Saulo, and Julia Nogués-Paegle. "WRF Model Sensitivity to Choice of Parameterization over South America: Validation against Surface Variables." Monthly Weather Review 138, no. 8 (August 1, 2010): 3342–55. http://dx.doi.org/10.1175/2010mwr3358.1.
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Abstract The Weather and Research Forecast model is tested over South America in different configurations to identify the one that gives the best estimates of observed surface variables. Systematic, nonsystematic, and total errors are computed for 48-h forecasts initialized with the NCEP Global Data Assimilation System (GDAS). There is no unique model design that best fits all variables over the whole domain, and nonsystematic errors for all configurations differ little from one another; such differences are in most cases smaller than the observed day-to-day variability. An ensemble mean consisting of runs with different parameterizations gives the best skill for the whole domain. Surface variables are highly sensitive to the choice of land surface models. Surface temperature is well represented by the Noah land model, but dewpoint temperature is best estimated by the simplest land surface model considered here, which specifies soil moisture based on climatology. This underlines the need for better understanding of humid processes at the subgrid scale. Surface wind errors decrease the intensity of the low-level jet, reducing expected heat and moisture advection over southeast South America (SESA), with negative precipitation errors over SESA and positive biases over the South Atlantic convergence zone (SACZ). This pattern of errors suggests feedbacks between wind errors, precipitation, and surface processes as follows: an increase of precipitation over the SACZ produces compensating descent in SESA, with more stable stratification, less rain, less soil moisture, and decreased rain. This is a clear example of how local errors are related to regional circulation, and suggests that improvement of model performance requires not only better parameterizations at the subgrid scales, but also improved regional models.