Journal articles on the topic 'Long-range weather forecasting Africa'
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Mwangi, E., F. Wetterhall, E. Dutra, F. Di Giuseppe, and F. Pappenberger. "Forecasting droughts in East Africa." Hydrology and Earth System Sciences Discussions 10, no. 8 (August 8, 2013): 10209–30. http://dx.doi.org/10.5194/hessd-10-10209-2013.
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Abstract. The humanitarian crisis caused by the recent droughts (2008–2009 and 2010–2011) in the East African region have illustrated that the ability to make accurate drought predictions with adequate lead time is essential. The use of dynamical model forecasts and drought indices, such as Standardized Precipitation Index (SPI), promises to lead to a better description of drought duration, magnitude and spatial extent. This study evaluates the use of the European Centre for Medium-Range Weather Forecasts (ECMWF) products in forecasting droughts in East Africa. ECMWF seasonal precipitation shows significant skill for both rain seasons when evaluated against measurements from the available in-situ stations from East Africa. The October–December rain season has higher skill that the March–May season. ECMWF forecasts add value to the statistical forecasts produced during the Greater Horn of Africa Climate Outlook Forums (GHACOF) which is the present operational product. Complementing the raw precipitation forecasts with SPI provides additional information on the spatial extend and intensity of the drought event.
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Mwangi, E., F. Wetterhall, E. Dutra, F. Di Giuseppe, and F. Pappenberger. "Forecasting droughts in East Africa." Hydrology and Earth System Sciences 18, no. 2 (February 18, 2014): 611–20. http://dx.doi.org/10.5194/hess-18-611-2014.
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Abstract. The humanitarian crises caused by the recent droughts (2008–2009 and 2010–2011) in East Africa have illustrated that the ability to make accurate drought forecasts with sufficient lead time is essential. The use of dynamical model precipitation forecasts in combination with drought indices, such as the Standardized Precipitation Index (SPI), can potentially lead to a better description of drought duration, magnitude and spatial extent. This study evaluates the use of the European Centre for Medium-Range Weather Forecasts (ECMWF) products in forecasting droughts in East Africa. ECMWF seasonal precipitation shows significant skill for March–May and October–December rain seasons when evaluated against measurements from the available in situ stations from East Africa. The forecast for October–December rain season has higher skill than for the March–May season. ECMWF forecasts add value to the consensus forecasts produced during the Greater Horn of Africa Climate Outlook Forum (GHACOF), which is the present operational product for precipitation forecast over East Africa. Complementing the original ECMWF precipitation forecasts with SPI provides additional information on the spatial extent and intensity of the drought event.
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Muofhe, Tshimbiluni Percy, Hector Chikoore, Mary-Jane Morongwa Bopape, Nthaduleni Samuel Nethengwe, Thando Ndarana, and Gift Tshifhiwa Rambuwani. "Forecasting Intense Cut-Off Lows in South Africa Using the 4.4 km Unified Model." Climate 8, no. 11 (November 7, 2020): 129. http://dx.doi.org/10.3390/cli8110129.
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Mid-tropospheric cut-off low (COL) pressure systems are linked to severe weather, heavy rainfall and extreme cold conditions over South Africa. They occur during all the above and often result in floods and snowfalls during the winter months, disrupting economic activities and causing extensive damage to infrastructure. This paper examines the evolution and circulation patterns associated with cases of severe COLs over South Africa. We evaluate the performance of the 4.4 km Unified Model (UM) which is currently used operationally by the South African Weather Service (SAWS) to simulate daily rainfall. Circulation variables and precipitation simulated by the UM were compared against European Centre for Medium-Range Weather Forecast’s (ECMWF’s) ERA Interim re-analyses and GPM precipitation at 24-hour timesteps. We present five recent severe COLs, which occurred between 2016 and 2019, that had high impact and found a higher model skill when simulating heavy precipitation during the initial stages than the dissipating stages of the systems. A key finding was that the UM simulated the precipitation differently during the different stages of development and location of the systems. This is mainly due to inaccurate placing of COL centers. Understanding the performance and limitations of the UM model in simulating COL characteristics can benefit severe weather forecasting and contribute to disaster risk reduction in South Africa.
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Batté, Lauriane, Constantin Ardilouze, and Michel Déqué. "Forecasting West African Heat Waves at Subseasonal and Seasonal Time Scales." Monthly Weather Review 146, no. 3 (March 1, 2018): 889–907. http://dx.doi.org/10.1175/mwr-d-17-0211.1.
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Abstract Early indication of an increased risk of extremely warm conditions could help alleviate some of the consequences of severe heat waves on human health. This study focuses on boreal spring heat wave events over West Africa and the Sahel and examines the long-range predictability and forecast quality of these events with two coupled forecasting systems designed at Météo-France, both based on the CNRM-CM coupled global climate model: the operational seasonal forecasting System 5 and the experimental contribution to the World Weather Research Programme/World Climate Research Programme (WWRP/WCRP) subseasonal-to-seasonal (S2S) project. Evaluation is based on past reforecasts spanning 22 years, from 1993 to 2014, compared to reference data from reanalyses. On the seasonal time scale, skill in reproducing interannual anomalies of heat wave duration is limited at a gridpoint level but is significant for regional averages. Subseasonal predictability of daily humidity-corrected apparent temperature drops sharply beyond the deterministic range. In addition to reforecast skill measures, the analysis of real-time forecasts for 2016, both in terms of anomalies with respect to the reforecast climatology and using a weather-type approach, provides additional insight on the systems’ performance in giving relevant information on the possible occurrence of such events.
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BHOWMIK, S. K. ROY, ANUPAM KUMAR, and ANANDA K.DAS. "Real-time mesoscale modeling for short range prediction of weather over Maitri region in Antarctica." MAUSAM 62, no. 4 (December 16, 2021): 535–46. http://dx.doi.org/10.54302/mausam.v62i4.339.
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The main objective of this paper is to implement Polar WRF model for the Maitri (Lat. 70° 45 S, Long. 11° 44 E) region at the horizontal resolution of 15 km using initial and boundary conditions of the Global Forecast System T-382 operational at the India Meteorological Department (IMD). The study evaluates the performance of the model using the conventional approach of case studies. The results of the case studies illustrated in this paper reveal that the model is capable of capturing synoptic and meso-scale weather systems. Forecast fields are consistent with the corresponding analysis fields. Synoptic charts of mean sea level pressure prepared by the Weather Service of South Africa at Pretoria are used for the model validation. The model derived meteograms of mean sea level pressure are compared against the corresponding observations. The study demonstrates the usefulness of the forecast products for short range forecasting of weather over the Maitri region. The forecast outputs are made available in the real-time mode in the national web site of IMD www.imd.gov.in. The study is expected to benefit weather forecasters at Maitri.
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Tennant, Warren J., Zoltan Toth, and Kevin J. Rae. "Application of the NCEP Ensemble Prediction System to Medium-Range Forecasting in South Africa: New Products, Benefits, and Challenges." Weather and Forecasting 22, no. 1 (February 1, 2007): 18–35. http://dx.doi.org/10.1175/waf979.1.
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Abstract The National Centers for Environmental Prediction (NCEP) Ensemble Forecasting System (EFS) is used operationally in South Africa for medium-range forecasts up to 14 days ahead. The use of model-generated probability forecasts has a clear benefit in the skill of the 1–7-day forecasts. This is seen in the forecast probability distribution being more successful in spanning the observed space than a single deterministic forecast and, thus, substantially reducing the instances of missed events in the forecast. In addition, the probability forecasts generated using the EFS are particularly useful in estimating confidence in forecasts. During the second week of the forecast the EFS is used as a heads-up for possible synoptic-scale events and also for predicting average weather conditions and probability density distributions of some elements such as maximum temperature and wind. This paper assesses the medium-range forecast process and the application of the NCEP EFS at the South African Weather Service. It includes a description of the various medium-range products, adaptive bias-correction methods applied to the forecasts, verification of the forecast products, and a discussion on the various challenges that face researchers and forecasters alike.
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ABDELWAHAB, MM, A. SALAHELDIN, and Z. METWALLY. "A case of Khamsin type weather in north Africa." MAUSAM 36, no. 3 (April 6, 2022): 291–94. http://dx.doi.org/10.54302/mausam.v36i3.1912.
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In most cases, desert depressions over north Africa form in the lee of the Atlas mountains, Such occurrences are found when a north or northwest air stream from over the Atlantic moves toward the Atlas range, or when a northeasterly wind blows over the western Mediterranean towards these mountains, As shown in Fig. 1, these depressions may follow numerous tracks during their eastward movement. These depressions usually produce severe heat waves and sandstorms (EL Fandi, 1940, Soliman 1958). The phenomenon of Khamsin weather in spring is one of the main problems associated with weather analysis and forecasting in the area of north Africa. In recent years, several formulations of these types of desert depressions have been discussed from the point of view of their sources and supply of heat and energy.
The present paper is an attempt to identify the effect of cyc1ogenetic activity on the trajectory of these depressions.
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Mkuhlani, Siyabusa, Nkulumo Zinyengere, Naomi Kumi, and Olivier Crespo. "Lessons from integrated seasonal forecast-crop modelling in Africa: A systematic review." Open Life Sciences 17, no. 1 (January 1, 2022): 1398–417. http://dx.doi.org/10.1515/biol-2022-0507.
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Abstract Seasonal forecasts coupled with crop models can potentially enhance decision-making in smallholder farming in Africa. The study sought to inform future research through identifying and critiquing crop and climate models, and techniques for integrating seasonal forecast information and crop models. Peer-reviewed articles related to crop modelling and seasonal forecasting were sourced from Google Scholar, Web of Science, AGRIS, and JSTOR. Nineteen articles were selected from a search outcome of 530. About 74% of the studies used mechanistic models, which are favored for climate risk management research as they account for crop management practices. European Centre for Medium-Range Weather Forecasts and European Centre for Medium-Range Weather Forecasts, Hamburg, are the predominant global climate models (GCMs) used across Africa. A range of approaches have been assessed to improve the effectiveness of the connection between seasonal forecast information and mechanistic crop models, which include GCMs, analogue, stochastic disaggregation, and statistical prediction through converting seasonal weather summaries into the daily weather. GCM outputs are produced in a format compatible with mechanistic crop models. Such outputs are critical for researchers to have information on the merits and demerits of tools and approaches on integrating seasonal forecast and crop models. There is however need to widen such research to other regions in Africa, crop, farming systems, and policy.
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Oosthuizen, Christiaan, Barend Van Wyk, Yskandar Hamam, Dawood Desai, and Yasser Alayli. "The Use of Gridded Model Output Statistics (GMOS) in Energy Forecasting of a Solar Car." Energies 13, no. 8 (April 17, 2020): 1984. http://dx.doi.org/10.3390/en13081984.
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For many years, primary weather forecasting services (Global Forecast System (GFS) and the European Centre for Medium-Range Weather Forecasts (ECMWF)) have been made available to the public through global Numerical Weather Prediction (NWP) models estimating a multitude of general weather variables in a variety of resolutions. Secondary services such as weather experts Meteomatics AG use data and improve the forecasts through various methods. They tailor for the specific needs of customers in the wind and solar power generation sector as well as data scientists, analysts, and meteorologists in all areas of business. These auxiliary services have improved performance and provide reliable data. However, this work extended these auxiliary services to so-called tertiary services in which the weather forecasts were further conditioned for the very niche application environment of mobile solar technology in solar car energy management. The Gridded Model Output Statistics (GMOS) Global Horizontal Irradiance (GHI) model developed in this work utilizes historical data from various ground station locations in South Africa to reduce the mean forecast error of the GHI component. An average Root Mean Square Error (RMSE) improvement of 11.28% was shown across all locations and weather conditions. It was also shown how the incorporation of the GMOS model could have increased the accuracy in regard to the State of Charge (SoC) energy simulation of a solar car during the Sasol Solar Challenge 2018 and the possible range benefits thereof.
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Thiemig, V., B. Bisselink, F. Pappenberger, and J. Thielen. "A pan-African medium-range ensemble flood forecast system." Hydrology and Earth System Sciences 19, no. 8 (August 3, 2015): 3365–85. http://dx.doi.org/10.5194/hess-19-3365-2015.
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Abstract. The African Flood Forecasting System (AFFS) is a probabilistic flood forecast system for medium- to large-scale African river basins, with lead times of up to 15 days. The key components are the hydrological model LISFLOOD, the African GIS database, the meteorological ensemble predictions by the ECMWF (European Centre for Medium-Ranged Weather Forecasts) and critical hydrological thresholds. In this paper, the predictive capability is investigated in a hindcast mode, by reproducing hydrological predictions for the year 2003 when important floods were observed. Results were verified by ground measurements of 36 sub-catchments as well as by reports of various flood archives. Results showed that AFFS detected around 70 % of the reported flood events correctly. In particular, the system showed good performance in predicting riverine flood events of long duration (> 1 week) and large affected areas (> 10 000 km2) well in advance, whereas AFFS showed limitations for small-scale and short duration flood events. The case study for the flood event in March 2003 in the Sabi Basin (Zimbabwe) illustrated the good performance of AFFS in forecasting timing and severity of the floods, gave an example of the clear and concise output products, and showed that the system is capable of producing flood warnings even in ungauged river basins. Hence, from a technical perspective, AFFS shows a large potential as an operational pan-African flood forecasting system, although issues related to the practical implication will still need to be investigated.
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Cafaro, Carlo, Beth J. Woodhams, Thorwald H. M. Stein, Cathryn E. Birch, Stuart Webster, Caroline L. Bain, Andrew Hartley, Samantha Clarke, Samantha Ferrett, and Peter Hill. "Do Convection-Permitting Ensembles Lead to More Skillful Short-Range Probabilistic Rainfall Forecasts over Tropical East Africa?" Weather and Forecasting 36, no. 2 (April 2021): 697–716. http://dx.doi.org/10.1175/waf-d-20-0172.1.
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AbstractConvection-permitting ensemble prediction systems (CP-ENS) have been implemented in the midlatitudes for weather forecasting time scales over the past decade, enabled by the increase in computational resources. Recently, efforts are being made to study the benefits of CP-ENS for tropical regions. This study examines CP-ENS forecasts produced by the Met Office over tropical East Africa, for 24 cases in the period April–May 2019. The CP-ENS, an ensemble with parameterized convection (Glob-ENS), and their deterministic counterparts are evaluated against rainfall estimates derived from satellite observations (GPM-IMERG). The CP configurations have the best representation of the diurnal cycle, although heavy rainfall amounts are overestimated compared to observations. Pairwise comparisons between the different configurations reveal that the CP-ENS is generally the most skillful forecast for both 3- and 24-h accumulations of heavy rainfall (97th percentile), followed by the CP deterministic forecast. More precisely, probabilistic forecasts of heavy rainfall, verified using a neighborhood approach, show that the CP-ENS is skillful at scales greater than 100 km, significantly better than the Glob-ENS, although not as good as found in the midlatitudes. Skill decreases with lead time and varies diurnally, especially for CP forecasts. The CP-ENS is underspread both in terms of forecasting the locations of heavy rainfall and in terms of domain-averaged rainfall. This study demonstrates potential benefits in using CP-ENS for operational forecasting of heavy rainfall over tropical Africa and gives specific suggestions for further research and development, including probabilistic forecast guidance.
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Kolstad, Erik W., C. Ole Wulff, Daniela I. V. Domeisen, and Tim Woollings. "Tracing North Atlantic Oscillation Forecast Errors to Stratospheric Origins." Journal of Climate 33, no. 21 (November 1, 2020): 9145–57. http://dx.doi.org/10.1175/jcli-d-20-0270.1.
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AbstractThe North Atlantic Oscillation (NAO) is the main driver of weather variability in parts of Eurasia, Greenland, North America, and North Africa on a range of time scales. Successful extended-range NAO predictions would equate to improved predictions of precipitation and temperature in these regions. It has become clear that the NAO is influenced by the stratosphere, but because this downward coupling is not fully reproduced by all forecast models the potential for improved NAO forecasts has not been fully realized. Here, an analysis of 21 winters of subseasonal forecast data from the European Centre for Medium-Range Weather Forecasts monthly forecasting system is presented. By dividing the forecasts into clusters according to their errors in North Atlantic Ocean sea level pressure 15–30 days into the forecasts, we identify relationships between these errors and the state of the stratospheric polar vortex when the forecasts were initialized. A key finding is that the model overestimates the persistence of both the negative NAO response following a weak polar vortex and the positive NAO response following a strong polar vortex. A case in point is the sudden stratospheric warming in early 2019, which was followed by five consecutive weeks of an overestimation of the negative NAO regime. A consequence on the ground was temperature predictions for northern Europe that were too cold. Another important finding is that the model appears to misrepresent the gradual downward impact of stratospheric vortex anomalies. This result suggests that an improved representation and prediction of stratosphere–troposphere coupling in models might yield substantial benefits for extended-range weather forecasting in the Northern Hemisphere midlatitudes.
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Schwitalla, Thomas, Kirsten Warrach-Sagi, Volker Wulfmeyer, and Michael Resch. "Near-global-scale high-resolution seasonal simulations with WRF-Noah-MP v.3.8.1." Geoscientific Model Development 13, no. 4 (April 21, 2020): 1959–74. http://dx.doi.org/10.5194/gmd-13-1959-2020.
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Abstract. The added value of global simulations on the convection-permitting (CP) scale is a subject of extensive research in the earth system science community. An increase in predictive skill can be expected due to advanced representations of feedbacks and teleconnections in the ocean–land–atmosphere system. However, the proof of this hypothesis by corresponding simulations is computationally and scientifically extremely demanding. We present a novel latitude-belt simulation from 57∘ S to 65∘ N using the Weather Research and Forecasting (WRF)-Noah-MP model system with a grid increment of 0.03∘ over a period of 5 months forced by sea surface temperature observations. In comparison to a latitude-belt simulation with 45 km resolution, at CP resolution the representation of the spatial-temporal scales and the organization of tropical convection are improved considerably. The teleconnection pattern is very close to that of the operational European Centre for Medium Range Weather Forecasting (ECMWF) analyses. The CP simulation is associated with an improvement of the precipitation forecast over South America, Africa, and the Indian Ocean and considerably improves the representation of cloud coverage along the tropics. Our results demonstrate a significant added value of future simulations on the CP scale up to the seasonal forecast range.
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Kilavi, Mary, Dave MacLeod, Maurine Ambani, Joanne Robbins, Rutger Dankers, Richard Graham, Titley Helen, Abubakr Salih, and Martin Todd. "Extreme Rainfall and Flooding over Central Kenya Including Nairobi City during the Long-Rains Season 2018: Causes, Predictability, and Potential for Early Warning and Actions." Atmosphere 9, no. 12 (November 30, 2018): 472. http://dx.doi.org/10.3390/atmos9120472.
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The Long-Rains wet season of March–May (MAM) over Kenya in 2018 was one of the wettest on record. This paper examines the nature, causes, impacts, and predictability of the rainfall events, and considers the implications for flood risk management. The exceptionally high monthly rainfall totals in March and April resulted from several multi-day heavy rainfall episodes, rather than from distinct extreme daily events. Three intra-seasonal rainfall events in particular resulted in extensive flooding with the loss of lives and livelihoods, a significant displacement of people, major disruption to essential services, and damage to infrastructure. The rainfall events appear to be associated with the combined effects of active Madden–Julian Oscillation (MJO) events in MJO phases 2–4, and at shorter timescales, tropical cyclone events over the southwest Indian Ocean. These combine to drive an anomalous westerly low-level circulation over Kenya and the surrounding region, which likely leads to moisture convergence and enhanced convection. We assessed how predictable such events over a range of forecast lead times. Long-lead seasonal forecast products for MAM 2018 showed little indication of an enhanced likelihood of heavy rain over most of Kenya, which is consistent with the low predictability of MAM Long-Rains at seasonal lead times. At shorter lead times of a few weeks, the seasonal and extended-range forecasts provided a clear signal of extreme rainfall, which is likely associated with skill in MJO prediction. Short lead weather forecasts from multiple models also highlighted enhanced risk. The flood response actions during the MAM 2018 events are reviewed. Implications of our results for forecasting and flood preparedness systems include: (i) Potential exists for the integration of sub-seasonal and short-term weather prediction to support flood risk management and preparedness action in Kenya, notwithstanding the particular challenge of forecasting at small scales. (ii) We suggest that forecasting agencies provide greater clarity on the difference in potentially useful forecast lead times between the two wet seasons in Kenya and East Africa. For the MAM Long-Rains, the utility of sub-seasonal to short-term forecasts should be emphasized; while at seasonal timescales, skill is currently low, and there is the challenge of exploiting new research identifying the primary drivers of variability. In contrast, greater seasonal predictability of the Short-Rains in the October–December season means that greater potential exists for early warning and preparedness over longer lead times. (iii) There is a need for well-developed and functional forecast-based action systems for heavy rain and flood risk management in Kenya, especially with the relatively short windows for anticipatory action during MAM.
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Weigel, Andreas P., Daniel Baggenstos, Mark A. Liniger, Frédéric Vitart, and Christof Appenzeller. "Probabilistic Verification of Monthly Temperature Forecasts." Monthly Weather Review 136, no. 12 (December 1, 2008): 5162–82. http://dx.doi.org/10.1175/2008mwr2551.1.
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Abstract Monthly forecasting bridges the gap between medium-range weather forecasting and seasonal predictions. While such forecasts in the prediction range of 1–4 weeks are vital to many applications in the context of weather and climate risk management, surprisingly little has been published on the actual monthly prediction skill of existing global circulation models. Since 2004, the European Centre for Medium-Range Weather Forecasts has operationally run a dynamical monthly forecasting system (MOFC). It is the aim of this study to provide a systematic and fully probabilistic evaluation of MOFC prediction skill for weekly averaged forecasts of surface temperature in dependence of lead time, region, and season. This requires the careful setup of an appropriate verification context, given that the verification period is short and ensemble sizes small. This study considers the annual cycle of operational temperature forecasts issued in 2006, as well as the corresponding 12 yr of reforecasts (hindcasts). The debiased ranked probability skill score (RPSSD) is applied for verification. This probabilistic skill metric has the advantage of being insensitive to the intrinsic unreliability due to small ensemble sizes—an issue that is relevant in the present context since MOFC hindcasts only have five ensemble members. The formulation of the RPSSD is generalized here such that the small hindcast ensembles and the large operational forecast ensembles can be jointly considered in the verification. A bootstrap method is applied to estimate confidence intervals. The results show that (i) MOFC forecasts are generally not worse than climatology and do outperform persistence, (ii) MOFC forecasts are skillful beyond a lead time of 18 days over some ocean regions and to a small degree also over tropical South America and Africa, (iii) extratropical continental predictability essentially vanishes after 18 days of integration, and (iv) even when the average predictability is low there can nevertheless be climatic conditions under which the forecasts contain useful information. With the present model, a significant skill improvement beyond 18 days of integration can only be achieved by increasing the averaging interval. Recalibration methods are expected to be without effect since the forecasts are essentially reliable.
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Winsemius, H. C., E. Dutra, F. A. Engelbrecht, E. Archer Van Garderen, F. Wetterhall, F. Pappenberger, and M. G. F. Werner. "The potential value of seasonal forecasts in a changing climate in southern Africa." Hydrology and Earth System Sciences 18, no. 4 (April 25, 2014): 1525–38. http://dx.doi.org/10.5194/hess-18-1525-2014.
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Abstract. Subsistence farming in southern Africa is vulnerable to extreme weather conditions. The yield of rain-fed agriculture depends largely on rainfall-related factors such as total seasonal rainfall, anomalous onsets and lengths of the rainy season and the frequency of occurrence of dry spells. Livestock, in turn, may be seriously impacted by climatic stress with, for example, exceptionally hot days, affecting condition, reproduction, vulnerability to pests and pathogens and, ultimately, morbidity and mortality. Climate change may affect the frequency and severity of extreme weather conditions, impacting on the success of subsistence farming. A potentially interesting adaptation measure comprises the timely forecasting and warning of such extreme events, combined with mitigation measures that allow farmers to prepare for the event occurring. This paper investigates how the frequency of extreme events may change in the future due to climate change over southern Africa and, in more detail, the Limpopo Basin using a set of climate change projections from several regional climate model downscalings based on an extreme climate scenario. Furthermore, the paper assesses the predictability of these indicators by seasonal meteorological forecasts of the European Centre for Medium-Range Weather Forecasts (ECMWF) seasonal forecasting system. The focus is on the frequency of dry spells as well as the frequency of heat stress conditions expressed in the temperature heat index. In areas where their frequency of occurrence increases in the future and predictability is found, seasonal forecasts will gain importance in the future, as they can more often lead to informed decision-making to implement mitigation measures. The multi-model climate projections suggest that the frequency of dry spells is not likely to increase substantially, whereas there is a clear and coherent signal among the models of an increase in the frequency of heat stress conditions by the end of the century. The skill analysis of the seasonal forecast system demonstrates that there is a potential to adapt to this change by utilizing the weather forecasts, given that both indicators can be skilfully predicted for the December–February season, at least 2 months ahead of the wet season. This is particularly the case for predicting above-normal and below-normal conditions. The frequency of heat stress conditions shows better predictability than the frequency of dry spells. Although results are promising for end users on the ground, forecasts alone are insufficient to ensure appropriate response. Sufficient support for appropriate measures must be in place, and forecasts must be communicated in a context-specific, accessible and understandable format.
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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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Jones, Anne E., and Andrew P. Morse. "Application and Validation of a Seasonal Ensemble Prediction System Using a Dynamic Malaria Model." Journal of Climate 23, no. 15 (August 1, 2010): 4202–15. http://dx.doi.org/10.1175/2010jcli3208.1.
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Abstract Seasonal multimodel forecasts from the Development of a European Multimodel Ensemble System for Seasonal-to-Interannual Prediction (DEMETER) project are used to drive a malaria model and create reforecasts of malaria incidence for Botswana, in southern Africa, in a unique integration of a fully dynamic, process-based malaria model with an ensemble forecasting system. The forecasts are verified against a 20-yr malaria index and compared against reference simulations obtained by driving the malaria model with data from the 40-yr European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA-40). Performance assessment reveals skill in the DEMETER-driven malaria forecasts for prediction of low (below the lower tercile), above-average (above the median), and high (above the upper tercile) malaria events, with the best results obtained for low malaria events [relative operating characteristics (ROC) area = 0.84, 95% confidence interval = 0.63–1.0]. For high malaria events, the DEMETER-driven malaria forecasts are skillful, but the forecasting system performs poorly for those years that it predicts the highest probabilities of a high malaria event. Potential economic value analysis demonstrates the potential value for the DEMETER-driven malaria forecasts over a wide range of user cost-loss ratios, which is primarily due to the ability of the system to save on the cost of action in low malaria years.
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Winsemius, H. C., E. Dutra, F. A. Engelbrecht, E. Archer Van Garderen, F. Wetterhall, F. Pappenberger, and M. G. F. Werner. "The potential value of seasonal forecasts in a changing climate." Hydrology and Earth System Sciences Discussions 10, no. 12 (December 4, 2013): 14747–82. http://dx.doi.org/10.5194/hessd-10-14747-2013.
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Abstract. Subsistence farming in Southern Africa is vulnerable to extreme weather conditions. The yield of rain-fed agriculture depends largely on rainfall-related factors such as total seasonal rainfall, anomalous onsets and lengths of the rainy season and the frequency of occurrence of dry spells. Livestock, in turn, may be seriously impacted by climatic stress with, for example, exceptionally hot days, affecting condition, reproduction, vulnerability to pests and pathogens and, ultimately, morbidity and mortality. Climate change may affect the frequency and severity of extreme weather conditions, impacting on the success of subsistence farming. A potentially interesting adaptation measure comprises the timely forecasting and warning of such extreme events, combined with mitigation measures that allow farmers to prepare for the event occurring. This paper investigates how the frequency of extreme events may change in the future due to climate change over southern Africa and, in more detail, the Limpopo basin using a set of climate change projections from several regional climate model downscalings. Furthermore the paper assesses the predictability of these indicators by seasonal meteorological forecasts of the European Centre for Medium-range Weather Forecasts (ECMWF) seasonal forecasting system. The focus is on the frequency of dry spells as well as the frequency of heat stress conditions expressed in the Temperature Heat Index. In areas where their frequency of occurrence increases in the future and predictability is found, seasonal forecasts will gain importance in the future as they can more often lead to informed decision making to implement mitigation measures. The multi-model climate projections suggest that the frequency of dry spells is not likely to increase substantially, whereas there is a clear and coherent signal among the models, of an increase in the frequency of heat stress conditions by the end of the century. The skill analysis of the seasonal forecast system demonstrates that there is a potential to adapt to this change by utilizing the weather forecasts given that both indicators can be skilfully predicted for the December-to-February season, at least two months ahead of the wet season. This is particularly the case for predicting above-normal and below-normal conditions. The frequency of heat stress conditions shows better predictability than the frequency of dry spells. Although results are promising for end users on the ground, forecasts alone are insufficient to ensure appropriate response. Sufficient support for appropriate measures must be in place, and forecasts must be communicated in a context-specific, accessible and understandable format.
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Noble, Erik, Leonard M. Druyan, and Matthew Fulakeza. "The Sensitivity of WRF Daily Summertime Simulations over West Africa to Alternative Parameterizations. Part I: African Wave Circulation." Monthly Weather Review 142, no. 4 (March 27, 2014): 1588–608. http://dx.doi.org/10.1175/mwr-d-13-00194.1.
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Abstract The performance of the NCAR Weather Research and Forecasting Model (WRF) as a West African regional-atmospheric model is evaluated. The study tests the sensitivity of WRF-simulated vorticity maxima associated with African easterly waves to 64 combinations of alternative parameterizations in a series of simulations in September. In all, 104 simulations of 12-day duration during 11 consecutive years are examined. The 64 combinations combine WRF parameterizations of cumulus convection, radiation transfer, surface hydrology, and PBL physics. Simulated daily and mean circulation results are validated against NASA’s Modern-Era Retrospective Analysis for Research and Applications (MERRA) and NCEP/Department of Energy Global Reanalysis 2. Precipitation is considered in a second part of this two-part paper. A wide range of 700-hPa vorticity validation scores demonstrates the influence of alternative parameterizations. The best WRF performers achieve correlations against reanalysis of 0.40–0.60 and realistic amplitudes of spatiotemporal variability for the 2006 focus year while a parallel-benchmark simulation by the NASA Regional Model-3 (RM3) achieves higher correlations, but less realistic spatiotemporal variability. The largest favorable impact on WRF-vorticity validation is achieved by selecting the Grell–Devenyi cumulus convection scheme, resulting in higher correlations against reanalysis than simulations using the Kain–Fritch convection. Other parameterizations have less-obvious impact, although WRF configurations incorporating one surface model and PBL scheme consistently performed poorly. A comparison of reanalysis circulation against two NASA radiosonde stations confirms that both reanalyses represent observations well enough to validate the WRF results. Validation statistics for optimized WRF configurations simulating the parallel period during 10 additional years are less favorable than for 2006.
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Noble, Erik, Leonard M. Druyan, and Matthew Fulakeza. "The Sensitivity of WRF Daily Summertime Simulations over West Africa to Alternative Parameterizations. Part II: Precipitation." Monthly Weather Review 145, no. 1 (December 29, 2016): 215–33. http://dx.doi.org/10.1175/mwr-d-15-0294.1.
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Abstract This paper evaluates the performance of the Weather Research and Forecasting (WRF) Model as a regional atmospheric model over West Africa. It tests WRF’s sensitivity to 64 configurations of alternative parameterizations in a series of 104 twelve-day September simulations during 11 consecutive years, 2000–10. The 64 configurations combine WRF parameterizations of cumulus convection, radiation, surface hydrology, and the PBL. Simulated daily and total precipitation results are validated against Global Precipitation Climatology Project (GPCP) and Tropical Rainfall Measuring Mission (TRMM) data. Particular attention is given to westward-propagating precipitation maxima associated with African easterly waves (AEWs). A wide range of daily precipitation validation scores demonstrates the influence of alternative parameterizations. The best WRF performers achieve time–longitude correlations (against GPCP) of between 0.35 and 0.42 and spatiotemporal variability amplitudes only slightly higher than observed estimates. A parallel simulation by the benchmark Regional Model version 3 achieves a higher correlation (0.52) and realistic spatiotemporal variability amplitudes. The largest favorable impact on WRF precipitation validation is achieved by selecting the Grell–Devenyi convection scheme, resulting in higher correlations against observations than using the Kain–Fritch convection scheme. Other parameterizations have less obvious impacts. Validation statistics for optimized WRF configurations simulating the parallel period during 2000–10 are more favorable for 2005, 2006, and 2008 than for other years. The selection of some of the same WRF configurations as high scorers in both circulation and precipitation validations supports the notion that simulations of West African daily precipitation benefit from skillful simulations of associated AEW vorticity centers and that simulations of AEWs would benefit from skillful simulations of convective precipitation.
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Salakpi, Edward E., Peter D. Hurley, James M. Muthoka, Adam B. Barrett, Andrew Bowell, Seb Oliver, and Pedram Rowhani. "Forecasting vegetation condition with a Bayesian auto-regressive distributed lags (BARDL) model." Natural Hazards and Earth System Sciences 22, no. 8 (August 23, 2022): 2703–23. http://dx.doi.org/10.5194/nhess-22-2703-2022.
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Abstract. Droughts form a large part of climate- or weather-related disasters reported globally. In Africa, pastoralists living in the arid and semi-arid lands (ASALs) are the worse affected. Prolonged dry spells that cause vegetation stress in these regions have resulted in the loss of income and livelihoods. To curb this, global initiatives like the Paris Agreement and the United Nations recognised the need to establish early warning systems (EWSs) to save lives and livelihoods. Existing EWSs use a combination of satellite earth observation (EO)-based biophysical indicators like the vegetation condition index (VCI) and socio-economic factors to measure and monitor droughts. Most of these EWSs rely on expert knowledge in estimating upcoming drought conditions without using forecast models. Recent research has shown that the use of robust algorithms like auto-regression, Gaussian processes, and artificial neural networks can provide very skilled models for forecasting vegetation condition at short- to medium-range lead times. However, to enable preparedness for early action, forecasts with a longer lead time are needed. In a previous paper, a Gaussian process model and an auto-regression model were used to forecast VCI in pastoral communities in Kenya. The objective of this research was to build on this work by developing an improved model that forecasts vegetation conditions at longer lead times. The premise of this research was that vegetation condition is controlled by factors like precipitation and soil moisture; thus, we used a Bayesian auto-regressive distributed lag (BARDL) modelling approach, which enabled us to include the effects of lagged information from precipitation and soil moisture to improve VCI forecasting. The results showed a ∼2-week gain in the forecast range compared to the univariate auto-regression model used as a baseline. The R2 scores for the Bayesian ARDL model were 0.94, 0.85, and 0.74, compared to the auto-regression model's R2 of 0.88, 0.77, and 0.65 for 6-, 8-, and 10-week lead time, respectively.
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Quenum, Gandomè Mayeul Leger Davy, Joël Arnault, Nana Ama Browne Klutse, Zhenyu Zhang, Harald Kunstmann, and Philip G. Oguntunde. "Potential of the Coupled WRF/WRF-Hydro Modeling System for Flood Forecasting in the Ouémé River (West Africa)." Water 14, no. 8 (April 8, 2022): 1192. http://dx.doi.org/10.3390/w14081192.
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Since the beginning of the 2000s, most of the West-African countries, particularly Benin, have experienced an increased frequency of extreme flood events. In this study, we focus on the case of the Ouémé river basin in Benin. To investigate flood events in this basin for early warning, the coupled atmosphere–hydrology model system WRF-Hydro is used, and analyzed for the period 2008–2010. Such a coupled model allows exploration of the contribution of atmospheric components into the flood event, and its ability to simulate and predict accurate streamflow. The potential of WRF-Hydro to correctly simulate streamflow in the Ouémé river basin is assessed by forcing the model with operational analysis datasets from the European Centre for Medium-Range Weather Forecasts (ECMWF). Atmospheric and land surface processes are resolved at a spatial resolution of 5 km. The additional surface and subsurface water flow routing are computed at a resolution of 500 m. Key parameters of the hydrological module of WRF-Hydro are calibrated offline and tested online with the coupled WRF-Hydro. The uncertainty of atmospheric modeling on coupled results is assessed with the stochastic kinetic energy backscatter scheme (SKEBS). WRF-Hydro is able to simulate the discharge in the Ouémé river in offline and fully coupled modes with a Kling–Gupta efficiency (KGE) around 0.70 and 0.76, respectively. In the fully coupled mode, the model captures the flood event that occurred in 2010. A stochastic perturbation ensemble of ten members for three rain seasons shows that the coupled model performance in terms of KGE ranges from 0.14 to 0.79. Additionally, an assessment of the soil moisture has been developed. This ability to realistically reproduce observed discharge in the Ouémé river basin demonstrates the potential of the coupled WRF-Hydro modeling system for future flood forecasting applications.
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Heinzeller, Dominikus, Diarra Dieng, Gerhard Smiatek, Christiana Olusegun, Cornelia Klein, Ilse Hamann, Seyni Salack, Jan Bliefernicht, and Harald Kunstmann. "The WASCAL high-resolution regional climate simulation ensemble for West Africa: concept, dissemination and assessment." Earth System Science Data 10, no. 2 (April 23, 2018): 815–35. http://dx.doi.org/10.5194/essd-10-815-2018.
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Abstract. Climate change and constant population growth pose severe challenges to 21st century rural Africa. Within the framework of the West African Science Service Center on Climate Change and Adapted Land Use (WASCAL), an ensemble of high-resolution regional climate change scenarios for the greater West African region is provided to support the development of effective adaptation and mitigation measures. This contribution presents the overall concept of the WASCAL regional climate simulations, as well as detailed information on the experimental design, and provides information on the format and dissemination of the available data. All data are made available to the public at the CERA long-term archive of the German Climate Computing Center (DKRZ) with a subset available at the PANGAEA Data Publisher for Earth & Environmental Science portal (https://doi.pangaea.de/10.1594/PANGAEA.880512). A brief assessment of the data are presented to provide guidance for future users. Regional climate projections are generated at high (12 km) and intermediate (60 km) resolution using the Weather Research and Forecasting Model (WRF). The simulations cover the validation period 1980–2010 and the two future periods 2020–2050 and 2070–2100. A brief comparison to observations and two climate change scenarios from the Coordinated Regional Downscaling Experiment (CORDEX) initiative is presented to provide guidance on the data set to future users and to assess their climate change signal. Under the RCP4.5 (Representative Concentration Pathway 4.5) scenario, the results suggest an increase in temperature by 1.5 ∘C at the coast of Guinea and by up to 3 ∘C in the northern Sahel by the end of the 21st century, in line with existing climate projections for the region. They also project an increase in precipitation by up to 300 mm per year along the coast of Guinea, by up to 150 mm per year in the Soudano region adjacent in the north and almost no change in precipitation in the Sahel. This stands in contrast to existing regional climate projections, which predict increasingly drier conditions. The high spatial and temporal resolution of the data, the extensive list of output variables, the large computational domain and the long time periods covered make this data set a unique resource for follow-up analyses and impact modelling studies over the greater West African region. The comprehensive documentation and standardisation of the data facilitate and encourage their use within and outside of the WASCAL community.
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Weston, Michael John, Stuart John Piketh, Frédéric Burnet, Stephen Broccardo, Cyrielle Denjean, Thierry Bourrianne, and Paola Formenti. "Sensitivity analysis of an aerosol-aware microphysics scheme in Weather Research and Forecasting (WRF) during case studies of fog in Namibia." Atmospheric Chemistry and Physics 22, no. 15 (August 10, 2022): 10221–45. http://dx.doi.org/10.5194/acp-22-10221-2022.
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Abstract. Aerosol-aware microphysics parameterisation schemes are increasingly being introduced into numerical weather prediction models, allowing for regional and case-specific parameterisation of cloud condensation nuclei (CCN) and cloud droplet interactions. In this paper, the Thompson aerosol-aware microphysics scheme, within the Weather Research and Forecasting (WRF) model, is used for two fog cases during September 2017 over Namibia. Measurements of CCN and fog microphysics were undertaken during the AErosols, RadiatiOn and CLOuds in southern Africa (AEROCLO-sA) field campaign at Henties Bay on the coast of Namibia during September 2017. A key concept of the microphysics scheme is the conversion of water-friendly aerosols to cloud droplets (hereafter referred to as CCN activation), which could be estimated from the observations. A fog monitor 100 (FM-100) provided cloud droplet size distribution, number concentration (Nt), liquid water content (LWC), and mean volumetric diameter (MVD). These measurements are used to evaluate and parameterise WRF model simulations of Nt, LWC, and MVD. A sensitivity analysis was conducted through variations to the initial CCN concentration, CCN radius, and the minimum updraft speed, which are important factors that influence droplet activation in the microphysics scheme of the model. The first model scenario made use of the default settings with a constant initial CCN number concentration of 300 cm−3 and underestimated the cloud droplet number concentration, while the LWC was in good agreement with the observations. This resulted in droplet size being larger than the observations. Another scenario used modelled data as CCN initial conditions, which were an order of magnitude higher than other scenarios. However, these provided the most realistic values of Nt, LWC, MVD, and droplet size distribution. From this, it was concluded that CCN activation of around 10 % in the simulations is too low, while the observed appears to be higher reaching between 20 % and 80 %, with a mean (median) of 0.55 (0.56) during fog events. To achieve this level of activation in the model, the minimum updraft speed for CCN activation was increased from 0.01 to 0.1 m s−1. This scenario provided Nt, LWC, MVD, and droplet size distribution in the range of the observations, with the added benefit of a realistic initial CCN concentration. These results demonstrate the benefits of a dynamic aerosol-aware scheme when parameterised with observations.
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Bechtold, Peter, Noureddine Semane, Philippe Lopez, Jean-Pierre Chaboureau, Anton Beljaars, and Niels Bormann. "Representing Equilibrium and Nonequilibrium Convection in Large-Scale Models." Journal of the Atmospheric Sciences 71, no. 2 (January 31, 2014): 734–53. http://dx.doi.org/10.1175/jas-d-13-0163.1.
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Abstract A new diagnostic convective closure, which is dependent on convective available potential energy (CAPE), is derived under the quasi-equilibrium assumption for the free troposphere subject to boundary layer forcing. The closure involves a convective adjustment time scale for the free troposphere and a coupling coefficient between the free troposphere and the boundary layer based on different time scales over land and ocean. Earlier studies with the ECMWF Integrated Forecasting System (IFS) have already demonstrated the model’s ability to realistically represent tropical convectively coupled waves and synoptic variability with use of the “standard” CAPE closure, given realistic entrainment rates. A comparison of low-resolution seasonal integrations and high-resolution short-range forecasts against complementary satellite and radar data shows that with the extended CAPE closure it is also possible, independent of model resolution and time step, to realistically represent nonequilibrium convection such as the diurnal cycle of convection and the convection tied to advective boundary layers, although representing the late night convection over land remains a challenge. A more in-depth regional analysis of the diurnal cycle and the closure is provided for the continental United States and particularly Africa, including comparison with data from satellites and a cloud-resolving model (CRM). Consequences for global numerical weather prediction (NWP) are not only a better phase representation of convection, but also better forecasts of its spatial distribution and local intensity.
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Insua-Costa, Damián, Gonzalo Miguez-Macho, and María Carmen Llasat. "Local and remote moisture sources for extreme precipitation: a study of the two catastrophic 1982 western Mediterranean episodes." Hydrology and Earth System Sciences 23, no. 9 (September 24, 2019): 3885–900. http://dx.doi.org/10.5194/hess-23-3885-2019.
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Abstract. Floods and flash floods are frequent in the south of Europe resulting from heavy rainfall events that often produce more than 200 mm in less than 24 h. Even though the meteorological conditions favourable for these situations have been widely studied, there is a lingering question that still arises: what humidity sources could explain so much precipitation? To answer this question, the regional atmospheric Weather Research and Forecasting (WRF) model with a recently implemented moisture tagging capability has been used to analyse the main moisture sources for two catastrophic flood events that occurred during the autumn of 1982 (October and November) in the western Mediterranean area, which is regularly affected by these types of adverse weather episodes. The procedure consists in selecting a priori potential moisture source regions for the extreme event under consideration, and then performing simulations using the tagging technique to quantify the relative contribution of each selected source to total precipitation. For these events we study the influence of four possible potential sources: (1) evaporation in the western Mediterranean; (2) evaporation in the central Mediterranean; (3) evaporation in the North Atlantic; and (4) advection from the tropical and subtropical Atlantic and Africa. Results show that these four moisture sources explain most of the accumulated precipitation, with the tropical and subtropical input being the most relevant in both cases. In the October event, evaporation in the western and central Mediterranean and in the North Atlantic also had an important contribution. However, in the November episode tropical and subtropical moisture accounted for more than half of the total accumulated rainfall, while evaporation in the western Mediterranean and North Atlantic played a secondary role and the contribution of the central Mediterranean was almost negligible. Therefore, remote sources were crucial: in the October event they played a similar role to local sources, whereas in the November case they were clearly dominant. In both episodes, long-distance moisture transport from the tropics and subtropics mostly occurred in mid-tropospheric layers, via well-defined moisture plumes with maximum mixing ratios at medium levels.
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Craddock, J. M. "Long-range weather forecasting from 1947 onwards." Weather 50, no. 12 (December 1995): 403–7. http://dx.doi.org/10.1002/j.1477-8696.1995.tb06065.x.
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Otkin, Jason A., Thomas J. Greenwald, Justin Sieglaff, and Hung-Lung Huang. "Validation of a Large-Scale Simulated Brightness Temperature Dataset Using SEVIRI Satellite Observations." Journal of Applied Meteorology and Climatology 48, no. 8 (August 1, 2009): 1613–26. http://dx.doi.org/10.1175/2009jamc2142.1.
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Abstract In this study, the accuracy of a simulated infrared brightness temperature dataset derived from a unique large-scale, high-resolution Weather Research and Forecasting (WRF) Model simulation is evaluated through a comparison with Spinning Enhanced Visible and Infrared Imager (SEVIRI) observations. Overall, the analysis revealed that the simulated brightness temperatures realistically depict many of the observed features, although several large discrepancies were also identified. The similar shapes of the simulated and observed probability distributions calculated for each infrared band indicate that the model simulation realistically depicted the cloud morphology and relative proportion of clear and cloudy pixels. A traditional error analysis showed that the largest model errors occurred over central Africa because of a general mismatch in the locations of deep tropical convection and intervening regions of clear skies and low-level cloud cover. A detailed inspection of instantaneous brightness temperature difference (BTD) imagery showed that the modeling system realistically depicted the radiative properties associated with various cloud types. For instance, thin cirrus clouds along the edges of deep tropical convection and within midlatitude cloud shields were characterized by much larger 10.8 − 12.0-μm BTD than optically thicker clouds. Simulated ice clouds were effectively discriminated from liquid clouds and clear pixels by the close relationship between positive 8.7 − 10.8-μm BTD and the coldest 10.8-μm brightness temperatures. Comparison of the simulated and observed BTD probability distributions revealed that the liquid and mixed-phase cloud-top properties were consistent with the observations, whereas the narrower BTD distributions for the colder 10.8-μm brightness temperatures indicated that the microphysics scheme was unable to simulate the full dynamic range of ice clouds.
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Toth, Zoltan. "Long-Range Weather Forecasting Using an Analog Approach." Journal of Climate 2, no. 6 (June 1989): 594–607. http://dx.doi.org/10.1175/1520-0442(1989)002<0594:lrwfua>2.0.co;2.
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Hu, Zhiyuan, Jianping Huang, Chun Zhao, Qinjian Jin, Yuanyuan Ma, and Ben Yang. "Modeling dust sources, transport, and radiative effects at different altitudes over the Tibetan Plateau." Atmospheric Chemistry and Physics 20, no. 3 (February 7, 2020): 1507–29. http://dx.doi.org/10.5194/acp-20-1507-2020.
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Abstract. Mineral dust plays an important role in the climate of the Tibetan Plateau (TP) by modifying the radiation budget, cloud macro- and microphysics, precipitation, and snow albedo. Meanwhile, the TP, with the highest topography in the world, can affect intercontinental transport of dust plumes and induce typical distribution characteristics of dust at different altitudes. In this study, we conduct a quasi-global simulation to investigate the characteristics of dust source contribution and transport over the TP at different altitudes by using a fully coupled meteorology–chemistry model, the Weather Research and Forecasting model with chemistry (WRF-Chem), with a tracer-tagging technique. Generally, the simulation reasonably captures the spatial distribution of satellite-retrieved dust aerosol optical depth (AOD) at different altitudes. Model results show that dust particles are emitted into atmosphere through updrafts over major desert regions and then transported to the TP. The East Asian dust (mainly from the Gobi and Taklamakan deserts) is transported southward and is lifted up to the TP, contributing a mass loading of 50 mg m−2 at a height of 3 km and 5 mg m−2 at a height of 12 km over the northern slope of the TP. Dust from North Africa and the Middle East are concentrated over both of the northern and southern slopes below 6 km, where mass loadings range from 10 to 100 and 1 to 10 mg m−2 below 3 km and above 9 km, respectively. As the dust is transported to the north and over the TP, mass loadings are 5–10 mg m−2 above a height of 6 km. The dust mass flux carried from East Asia to the TP is 7.9 Tg yr−1, mostly occurring at heights of 3–6 km. The dust particles from North Africa and the Middle East are transported eastward following the westerly jet and then are carried into the TP at the west side with dust mass fluxes of 7.8 and 26.6 Tg yr−1, respectively. The maximum mass flux of the North African dust mainly occurs at 0–3 km (3.9 Tg yr−1), while the Middle Eastern dust occurs at 6–9 km (12.3 Tg yr−1). The dust outflow occurs on the east side (−17.89 Tg yr−1) and south side (−11.22 Tg yr−1) of the TP, with a peak value (8.7 Tg yr−1) at 6–9 km. Moreover, the dust (by mass) is concentrated within the size range of 1.25–5.0 µm and the dust (by particle number) is concentrated in the size range of 0.156–1.25 µm. Compared with other aerosols, the dust contributes to more than 50 % of the total AOD over the TP. The direct radiative forcing induced by the dust is −1.28 W m−2 at the top of the atmosphere (cooling), 0.41 W m−2 in the atmosphere (warming), and −1.68 W m−2 at the surface (cooling). Our quantitative analyses of the dust contributions from different source regions and the associated radiative forcing can help us to better understand the role of dust on the climate over the TP and surrounding regions.
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Nicholls, N., and F. Woodcock. "Verification of an empirical long-range weather forecasting technique." Quarterly Journal of the Royal Meteorological Society 107, no. 454 (July 6, 2007): 973–76. http://dx.doi.org/10.1002/qj.49710745415.
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Roberts, G., M. J. Wooster, W. Xu, P. H. Freeborn, J. J. Morcrette, L. Jones, A. Benedetti, and J. Kaiser. "LSA SAF Meteosat FRP Products: Part 2 – Evaluation and demonstration of use in the Copernicus Atmosphere Monitoring Service (CAMS)." Atmospheric Chemistry and Physics Discussions 15, no. 11 (June 12, 2015): 15909–76. http://dx.doi.org/10.5194/acpd-15-15909-2015.
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Abstract. Characterising the dynamics of landscape scale wildfires at very high temporal resolutions is best achieved using observations from Earth Observation (EO) sensors mounted onboard geostationary satellites. As a result, a number of operational active fire products have been developed from the data of such sensors. An example of which are the Fire Radiative Power (FRP) products, the FRP-PIXEL and FRP-GRID products, generated by the Land Surface Analysis Satellite Applications Facility (LSA SAF) from imagery collected by the Spinning Enhanced Visible and Infrared Imager (SEVIRI) on-board the Meteosat Second Generation (MSG) series of geostationary EO satellites. The processing chain developed to deliver these FRP products detects SEVIRI pixels containing actively burning fires and characterises their FRP output across four geographic regions covering Europe, part of South America and northern and southern Africa. The FRP-PIXEL product contains the highest spatial and temporal resolution FRP dataset, whilst the FRP-GRID product contains a spatio-temporal summary that includes bias adjustments for cloud cover and the non-detection of low FRP fire pixels. Here we evaluate these two products against active fire data collected by the Moderate Resolution Imaging Spectroradiometer (MODIS), and compare the results to those for three alternative active fire products derived from SEVIRI imagery. The FRP-PIXEL product is shown to detect a substantially greater number of active fire pixels than do alternative SEVIRI-based products, and comparison to MODIS on a per-fire basis indicates a strong agreement and low bias in terms of FRP values. However, low FRP fire pixels remain undetected by SEVIRI, with errors of active fire pixel detection commission and omission compared to MODIS ranging between 9–13 and 65–77% respectively in Africa. Higher errors of omission result in greater underestimation of regional FRP totals relative to those derived from simultaneously collected MODIS data, ranging from 35% over the Northern Africa region to 89% over the European region. High errors of active fire omission and FRP underestimation are found over Europe and South America, and result from SEVIRI's larger pixel area over these regions. An advantage of using FRP for characterising wildfire emissions is the ability to do so very frequently and in near real time (NRT). To illustrate the potential of this approach, wildfire fuel consumption rates derived from the SEVIRI FRP-PIXEL product are used to characterise smoke emissions of the 2007 Peloponnese wildfires within the European Centre for Medium-Range Weather Forecasting (ECMWF) Integrated Forecasting System (IFS), as a demonstration of what can be achieved when using geostationary active fire data within the Copernicus Atmosphere Monitoring System (CAMS). Qualitative comparison of the modelled smoke plumes with MODIS optical imagery illustrates that the model captures the temporal and spatial dynamics of the plume very well, and that high temporal resolution emissions estimates such as those available from geostationary orbit are important for capturing the sub-daily variability in smoke plume parameters such as aerosol optical depth (AOD), which are increasingly less well resolved using daily or coarser temporal resolution emissions datasets. Quantitative comparison of modelled AOD with coincident MODIS and AERONET AOD indicates that the former is overestimated by ∼ 20–30%, but captures the observed AOD dynamics with a high degree of fidelity. The case study highlights the potential of using geostationary FRP data to drive fire emissions estimates for use within atmospheric transport models such as those currently implemented as part of the Monitoring Atmospheric Composition and Climate (MACC) programme within the CAMS.
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Roberts, G., M. J. Wooster, W. Xu, P. H. Freeborn, J. J. Morcrette, L. Jones, A. Benedetti, H. Jiangping, D. Fisher, and J. W. Kaiser. "LSA SAF Meteosat FRP products – Part 2: Evaluation and demonstration for use in the Copernicus Atmosphere Monitoring Service (CAMS)." Atmospheric Chemistry and Physics 15, no. 22 (November 30, 2015): 13241–67. http://dx.doi.org/10.5194/acp-15-13241-2015.
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Abstract. Characterising the dynamics of landscape-scale wildfires at very high temporal resolutions is best achieved using observations from Earth Observation (EO) sensors mounted onboard geostationary satellites. As a result, a number of operational active fire products have been developed from the data of such sensors. An example of which are the Fire Radiative Power (FRP) products, the FRP-PIXEL and FRP-GRID products, generated by the Land Surface Analysis Satellite Applications Facility (LSA SAF) from imagery collected by the Spinning Enhanced Visible and Infrared Imager (SEVIRI) onboard the Meteosat Second Generation (MSG) series of geostationary EO satellites. The processing chain developed to deliver these FRP products detects SEVIRI pixels containing actively burning fires and characterises their FRP output across four geographic regions covering Europe, part of South America and Northern and Southern Africa. The FRP-PIXEL product contains the highest spatial and temporal resolution FRP data set, whilst the FRP-GRID product contains a spatio-temporal summary that includes bias adjustments for cloud cover and the non-detection of low FRP fire pixels. Here we evaluate these two products against active fire data collected by the Moderate Resolution Imaging Spectroradiometer (MODIS) and compare the results to those for three alternative active fire products derived from SEVIRI imagery. The FRP-PIXEL product is shown to detect a substantially greater number of active fire pixels than do alternative SEVIRI-based products, and comparison to MODIS on a per-fire basis indicates a strong agreement and low bias in terms of FRP values. However, low FRP fire pixels remain undetected by SEVIRI, with errors of active fire pixel detection commission and omission compared to MODIS ranging between 9–13 % and 65–77 % respectively in Africa. Higher errors of omission result in greater underestimation of regional FRP totals relative to those derived from simultaneously collected MODIS data, ranging from 35 % over the Northern Africa region to 89 % over the European region. High errors of active fire omission and FRP underestimation are found over Europe and South America and result from SEVIRI's larger pixel area over these regions. An advantage of using FRP for characterising wildfire emissions is the ability to do so very frequently and in near-real time (NRT). To illustrate the potential of this approach, wildfire fuel consumption rates derived from the SEVIRI FRP-PIXEL product are used to characterise smoke emissions of the 2007 "mega-fire" event focused on Peloponnese (Greece) and used within the European Centre for Medium-Range Weather Forecasting (ECMWF) Integrated Forecasting System (IFS) as a demonstration of what can be achieved when using geostationary active fire data within the Copernicus Atmosphere Monitoring Service (CAMS). Qualitative comparison of the modelled smoke plumes with MODIS optical imagery illustrates that the model captures the temporal and spatial dynamics of the plume very well, and that high temporal resolution emissions estimates such as those available from a geostationary orbit are important for capturing the sub-daily variability in smoke plume parameters such as aerosol optical depth (AOD), which are increasingly less well resolved using daily or coarser temporal resolution emissions data sets. Quantitative comparison of modelled AOD with coincident MODIS and AERONET (Aerosol Robotic Network) AOD indicates that the former is overestimated by ~ 20–30 %, but captures the observed AOD dynamics with a high degree of fidelity. The case study highlights the potential of using geostationary FRP data to drive fire emissions estimates for use within atmospheric transport models such as those implemented in the Monitoring Atmospheric Composition and Climate (MACC) series of projects for the CAMS.
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Hu, Zhiyuan, Jianping Huang, Chun Zhao, Yuanyuan Ma, Qinjian Jin, Yun Qian, L. Ruby Leung, Jianrong Bi, and Jianmin Ma. "Trans-Pacific transport and evolution of aerosols: spatiotemporal characteristics and source contributions." Atmospheric Chemistry and Physics 19, no. 19 (October 10, 2019): 12709–30. http://dx.doi.org/10.5194/acp-19-12709-2019.
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Abstract. Aerosols in the middle and upper troposphere have a long enough lifetime for trans-Pacific transport from East Asia to North America to influence air quality on the west coast of the United States (US). Here, we conduct quasi-global simulations (180∘ W–180∘ E and 70∘ S–75∘ N) from 2010 to 2014 using an updated version of WRF-Chem (Weather Research and Forecasting model fully coupled with chemistry) to analyze the spatiotemporal characteristics and source contributions of trans-Pacific aerosol transport. We find that trans-Pacific total aerosols have a maximum mass concentration (about 15 µg m−3) in the boreal spring with a peak between 3 and 4 km above the surface around 40∘ N. Sea salt and dust dominate the total aerosol mass concentration below 1 km and above 4 km, respectively. About 80.8 Tg of total aerosols (48.7 Tg of dust) are exported annually from East Asia, of which 26.7 Tg of aerosols (13.4 Tg of dust) reach the west coast of the US. Dust contributions from four desert regions in the Northern Hemisphere are analyzed using a tracer-tagging technique. About 4.9, 3.9, and 4.5 Tg year−1 of dust aerosol emitted from north Africa, the Middle East and central Asia, and East Asia, respectively, can be transported to the west coast of the US. The trans-Pacific aerosols dominate the column-integrated aerosol mass (∼65.5 %) and number concentration (∼80 %) over western North America. Radiation budget analysis shows that the inflow aerosols could contribute about 86.4 % (−2.91 W m−2) at the surface, 85.5 % (+1.36 W m−2) in the atmosphere, and 87.1 % (−1.55 W m−2) at the top of atmosphere to total aerosol radiative effect over western North America. However, near the surface in central and eastern North America, aerosols are mainly derived from local emissions, and the radiative effect of imported aerosols decreases rapidly. This study motivates further investigations of the potential impacts of trans-Pacific aerosols from East Asia on regional air quality and the hydrological cycle in North America.
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Levi, M. "Problems and prospects in long and medium range weather forecasting." Dynamics of Atmospheres and Oceans 9, no. 2 (July 1985): 211–12. http://dx.doi.org/10.1016/0377-0265(85)90005-3.
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Bates, J. R. "Problems and prospects in long and medium range weather forecasting." Earth-Science Reviews 22, no. 2 (September 1985): 162–63. http://dx.doi.org/10.1016/0012-8252(85)90028-5.
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Hewage, Pradeep, Ardhendu Behera, Marcello Trovati, Ella Pereira, Morteza Ghahremani, Francesco Palmieri, and Yonghuai Liu. "Temporal convolutional neural (TCN) network for an effective weather forecasting using time-series data from the local weather station." Soft Computing 24, no. 21 (April 23, 2020): 16453–82. http://dx.doi.org/10.1007/s00500-020-04954-0.
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Abstract Non-predictive or inaccurate weather forecasting can severely impact the community of users such as farmers. Numerical weather prediction models run in major weather forecasting centers with several supercomputers to solve simultaneous complex nonlinear mathematical equations. Such models provide the medium-range weather forecasts, i.e., every 6 h up to 18 h with grid length of 10–20 km. However, farmers often depend on more detailed short-to medium-range forecasts with higher-resolution regional forecasting models. Therefore, this research aims to address this by developing and evaluating a lightweight and novel weather forecasting system, which consists of one or more local weather stations and state-of-the-art machine learning techniques for weather forecasting using time-series data from these weather stations. To this end, the system explores the state-of-the-art temporal convolutional network (TCN) and long short-term memory (LSTM) networks. Our experimental results show that the proposed model using TCN produces better forecasting compared to the LSTM and other classic machine learning approaches. The proposed model can be used as an efficient localized weather forecasting tool for the community of users, and it could be run on a stand-alone personal computer.
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Shao, Congying, Yanmin Shuai, Latipa Tuerhanjiang, Xuexi Ma, Weijie Hu, Qingling Zhang, Aigong Xu, et al. "Cross-Comparison of Global Surface Albedo Operational Products-MODIS, GLASS, and CGLS." Remote Sensing 13, no. 23 (November 30, 2021): 4869. http://dx.doi.org/10.3390/rs13234869.
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Surface albedo, as an important parameter for land surface geo-biophysical and geo-biochemical processes, has been widely used in the research communities involved in surface energy balance, weather forecasting, atmospheric circulation, and land surface process models. In recent years, operational products using satellite-based surface albedo have, from time to time, been rapidly developed, contributing significantly to the estimation of energy balance at regional or global scales. The increasing number of research topics on dynamic monitoring at a decades-long scale requires a combination of albedo products generated from various sensors or programs, while the quantitative assessment of agreement or divergence among different surface albedo products still needs further understanding. In this paper, we investigated the consistency of three classical operational surface albedo products that have been frequently used by researchers globally via the official issued datasets-MODIS, GLASS (Global LAnd Surface Satellite), and CGLS (Copernicus Global Land Service). The cross-comparison was performed on all the identical dates available during 2000–2017 to represent four season-phases. We investigated the pixel-based validity of each product, consistency of global annual mean, spatial distribution and different temporal dynamics among the discussed products in white-sky (WSA) and black-sky (BSA) albedo at visible (VIS), near-infrared (NIR), and shortwave (SW) regimes. Further, varying features along with the change of seasons was also examined. In addition, the variation in accuracy of shortwave albedo magnitude was explored using ground measurements collected by the Baseline Surface Radiation Network (BSRN) and the Surface Radiation Budget Network (SUFRAD). Results show that: (1) All three products can provide valid long-term albedo for dominant land surface, while GLASS can provide additional estimation over sea surfaces, with the highest percentage of valid land surface pixels, at up to 93% in 24 October. The invalid pixels mainly existed in the 50°N–60°N latitude belt in December for GLASS, Central Africa in April and August for MODIS, and northern high latitudes for CGLS. (2) The global mean albedo of CGLS at the investigated bands has significantly higher values than those of MODIS and GLASS, with a relative difference of ~20% among the three products. The global mean albedo of MODIS and GLASS show a generally increasing trend from April to December, with an abrupt rise at NIR and SW of CGLS in June of 2014. Compared with SW and VIS bands, the linear temporal trend of the NIR global albedo mean in three products continues to increase, but the slope of CGLS is 10–100 times greater than that of the other two products. (3) The differences in albedo, which are higher in April, October, and December than in August, exhibit a small variation over the main global land surface regions, except for Central Eurasia, North Africa, and middle North America. The magnitude of global absolute difference among the three products usually varies within 0.02–0.06, but with the largest value occasionally exceeding 0.1. The relative difference is mainly within 10–20%, and can deviate more than 40% away from the baseline. In addition, CGLS has a greater opportunity to achieve the largest difference compared with MODIS and GLASS. (4) The comparison with ground measurements indicates that MODIS generally performs better than GLASS and CGLS at the sites discussed. This study demonstrates that apparent differences exist among the three investigated albedo products due to the ingested source data, algorithm, atmosphere correction etc., and also points at caution regarding data fusion when multiple albedo products were organized to serve the following applications.
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James, I. N. "A review of: “Problems and prospects in long and medium range weather forecasting”." Geophysical & Astrophysical Fluid Dynamics 32, no. 3-4 (July 1985): 333–35. http://dx.doi.org/10.1080/03091928508208789.
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Ryu, Seungyo, Dongseung Kim, and Joongheon Kim. "Weather-Aware Long-Range Traffic Forecast Using Multi-Module Deep Neural Network." Applied Sciences 10, no. 6 (March 12, 2020): 1938. http://dx.doi.org/10.3390/app10061938.
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This study proposes a novel multi-module deep neural network framework which aims at improving intelligent long-term traffic forecasting. Following our previous system, the internal architecture of the new system adds deep learning modules that enable data separation during computation. Thus, prediction becomes more accurate in many sections of the road network and gives dependable results even under possible changes in weather conditions during driving. The performance of the framework is then evaluated for different cases, which include all plausible cases of driving, i.e., regular days, holidays, and days involving severe weather conditions. Compared with other traffic predicting systems that employ the convolutional neural networks, k-nearest neighbor algorithm, and the time series model, it is concluded that the system proposed herein achieves better performance and helps drivers schedule their trips well in advance.
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Bercos-Hickey, Emily, Terrence R. Nathan, and Shu-Hua Chen. "On the Relationship between the African Easterly Jet, Saharan Mineral Dust Aerosols, and West African Precipitation." Journal of Climate 33, no. 9 (May 1, 2020): 3533–46. http://dx.doi.org/10.1175/jcli-d-18-0661.1.
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AbstractThe relationship between the African easterly jet (AEJ), Saharan mineral dust (SMD) aerosols, and West African precipitation (WAP) is examined using European Centre for Medium-Range Weather Forecasts interim reanalysis (ERA-Interim) data, the NASA Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2), and the NASA Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) for July–September 1998–2017. The spatial orientation and structure of AEJs in different SMD–WAP environments are compared. In dustier years, the AEJ is farther east and stronger, rotates clockwise, and has larger zonal and vertical shears. In wetter years, the AEJ is farther north, has a shorter zonal extent, and has larger meridional shear. These changes to the AEJ are a response to the combined effects of the SMD and WAP on the thermal field, which is confirmed through sensitivity tests carried out with the Weather Research and Forecasting Model coupled to an interactive dust model.
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Pietruska, Jamie L. "US Weather Bureau Chief Willis Moore and the Reimagination of Uncertainty in Long-Range Forecasting." Environment and History 17, no. 1 (February 1, 2011): 79–105. http://dx.doi.org/10.3197/096734011x12922359172970.
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CALANCA, P., D. BOLIUS, A. P. WEIGEL, and M. A. LINIGER. "Application of long-range weather forecasts to agricultural decision problems in Europe." Journal of Agricultural Science 149, no. 1 (October 5, 2010): 15–22. http://dx.doi.org/10.1017/s0021859610000729.
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SUMMARYAgriculture can benefit substantially from long-range weather forecasts, for the month or the season, which can help to optimize farming operations and deal more effectively with the adverse impacts of climate variability, including extreme weather events. In the context of climate change, long-range weather forecasts also represent key elements for the development of adaptation strategies. In spite of an undeniable potential, long-range forecasts issued for instance by the European Centre for Medium-Range Weather Forecasts (ECMWF) have yet to find widespread application in European agriculture. To address partially the question of why this is the case, the performance of the ECMWF monthly ensemble forecasting system was examined. It was noted that predictability is currently limited to about 3 weeks for temperature and 2 weeks for precipitation and solar radiation. This may appear deceptive at first sight, but it was noticed that precipitation forecasts over a month are, overall, at least as valuable as information obtained from observed climatology. Encouraged by this finding, the possibility of using monthly forecasts to predict soil water availability was tested. In an operational context, this could serve as a basis for scheduling irrigation. Positive skills were found for lead times of up to 1 month. It was concluded that more systematic investigations of the possibilities offered by long-range forecasts should be undertaken in the future. However, this will require additional efforts to increase the quality of the forecasts, design appropriate application tools and promote the dissemination of the outcome within the agriculture community.
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Moeletsi, Mokhele Edmond, Lindumusa Myeni, Ludwig Christian Kaempffer, Derick Vermaak, Gert de Nysschen, Chrisna Henningse, Irene Nel, and Dudley Rowswell. "Climate Dataset for South Africa by the Agricultural Research Council." Data 7, no. 8 (August 17, 2022): 117. http://dx.doi.org/10.3390/data7080117.
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Long-term, reliable, continuous and real-time weather and climatic data are essential for efficient management and sustainable use of natural resources. This paper describes the weather station network (WSN) of the Agricultural Research Council (ARC) of South Africa, including information on instrumentation, data retrieval and processing protocols, calibration and maintenance protocols, as well as applications of the collected data. To this end, the WSN of the ARC consists of over 600 automatic weather stations that are distributed across the country to cover a wide range of agro-climatic zones. At each weather station, air temperature, rainfall, relative humidity, solar irradiance, wind speed and direction are monitored and archived on an hourly basis. The main objective of this WSN is to archive climate information for South Africa as well as supply the agricultural community with weather data to support decision-making.
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Lee, Ming-Hsi, and Yenming J. Chen. "Precipitation Modeling for Extreme Weather Based on Sparse Hybrid Machine Learning and Markov Chain Random Field in a Multi-Scale Subspace." Water 13, no. 9 (April 29, 2021): 1241. http://dx.doi.org/10.3390/w13091241.
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This paper proposes to apply a Markov chain random field conditioning method with a hybrid machine learning method to provide long-range precipitation predictions under increasingly extreme weather conditions. Existing precipitation models are limited in time-span, and long-range simulations cannot predict rainfall distribution for a specific year. This paper proposes a hybrid (ensemble) learning method to perform forecasting on a multi-scaled, conditioned functional time series over a sparse l1 space. Therefore, on the basis of this method, a long-range prediction algorithm is developed for applications, such as agriculture or construction works. Our findings show that the conditioning method and multi-scale decomposition in the parse space l1 are proved useful in resisting statistical variation due to increasingly extreme weather conditions. Because the predictions are year-specific, we verify our prediction accuracy for the year we are interested in, but not for other years.
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Saxen, Thomas R., Cynthia K. Mueller, Thomas T. Warner, Matthias Steiner, Edward E. Ellison, Eric W. Hatfield, Terri L. Betancourt, Susan M. Dettling, and Niles A. Oien. "The Operational Mesogamma-Scale Analysis and Forecast System of the U.S. Army Test and Evaluation Command. Part IV: The White Sands Missile Range Auto-Nowcast System." Journal of Applied Meteorology and Climatology 47, no. 4 (April 1, 2008): 1123–39. http://dx.doi.org/10.1175/2007jamc1656.1.
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Abstract During the summer months at the U.S. Army Test and Evaluation Command’s (ATEC) White Sands Missile Range (WSMR), forecasting thunderstorm activity is one of the primary duties of the range forecasters. The safety of personnel working on the range and the protection of expensive test equipment depend critically on the quality of forecasts of thunderstorms and associated hazards, including cloud-to-ground lightning, hail, strong winds, heavy rainfall, flash flooding, and tornadoes. The National Center for Atmospheric Research (NCAR) Auto-Nowcast (ANC) system is one of the key forecast tools in the ATEC Four-Dimensional Weather System (4DWX) at WSMR, where its purpose is to aid WSMR meteorologists in their mission of very short term thunderstorm forecasting. Besides monitoring the weather activity throughout the region and warning personnel of potentially hazardous thunderstorms, forecasters play a key role in assisting with the day-to-day planning of test operations on the range by providing guidance with regard to weather conditions favorable to testing. Moreover, based on climatological information about the local weather conditions, forecasters advise their range customers about scheduling tests at WSMR months in advance. This paper reviews the NCAR ANC system, provides examples of the ANC system’s use in thunderstorm forecasting, and describes climatological analyses of WSMR summertime thunderstorm activity relevant for long-range planning of tests. The climatological analysis illustrates that radar-detected convective cells with reflectivity of ≥35 dBZ at WSMR are 1) short lived, with 76% having lifetimes of less than 30 min; 2) small, with 67% occupying areas of less than 25 km2; 3) slow moving, with 79% exhibiting speeds of less than 4 m s−1; 4) moderately intense, with 80% showing reflectivities in excess of 40 dBZ; and 5) deep, with 80% of the storms reaching far enough above the freezing level to be capable of generating lightning.
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DE CABO GARCIA, ALEJANDRO, ALFONSO DELGADO BONAL, BELEN PEREZ LANCHO, JORGE PLA GARCIA, and GERMAN MARTINEZ. "PREDICTION OF MARS METEOROLOGICAL VARIABLES USING ARTIFICIAL NEURAL NETWORKS." DYNA NEW TECHNOLOGIES 9, no. 1 (February 23, 2022): [15p.]. http://dx.doi.org/10.6036/nt10369.
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ABSTRACT Weather forecasting is the task of determining future states of the atmosphere for a given location and time. The techniques to carry out the prediction range from deterministic approaches using complex fluid dynamics models to data-driven approaches using artificial intelligence. While the former is mainly focused on the creation of General Circulation Models, the later are starting to replace them in many situations for Earth’s meteorology and astrophysics. Here, we develop an artificial neural network to perform Mars’ weather forecasting using environmental measurements from the Vikings and Mars Science Laboratory missions. The methodology followed in of this study is a data-driven approach; we make use of computer science expertise which has been long applied to Earth, but not on Mars yet. To do so, we create an artificial neuronal network that predicts the meteorological conditions of the following day using the previous day as input. We show that temperature and pressure are among the most important variables, and that ANN can perform with a 0.5 to 1% accuracy in forecasting diurnal changes in the selected variables. Keywords: ANN, Mars, Weather, Forecast, Curiosity
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Chui, Timothy C. Y., David Siuta, Gregory West, Henryk Modzelewski, Roland Schigas, and Roland Stull. "On Producing Reliable and Affordable Numerical Weather Forecasts on Public Cloud-Computing Infrastructure." Journal of Atmospheric and Oceanic Technology 36, no. 3 (March 2019): 491–509. http://dx.doi.org/10.1175/jtech-d-18-0142.1.
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AbstractCloud-computing resources are increasingly used in atmospheric research and real-time weather forecasting. The aim of this study is to explore new ways to reduce cloud-computing costs for real-time numerical weather prediction (NWP). One way is to compress output files to reduce data egress costs. File compression techniques can reduce data egress costs by over 50%. Data egress costs can be further minimized by postprocessing in the cloud and then exporting the smaller resulting files while discarding the bulk of the raw NWP output. Another way to reduce costs is to use preemptible resources, which are virtual machines (VMs) on the Google Cloud Platform (GCP) that clients can use at an 80% discount (compared to nonpreemptible VMs), but which can be turned off by the GCP without warning. By leveraging the restart functionality in the Weather Research and Forecasting (WRF) Model, preemptible resources can be used to save 60%–70% in weather simulation costs without compromising output reliability. The potential cost savings are demonstrated in forecasts over the Canadian Arctic and in a case study of NWP runs for the West African monsoon (WAM) of 2017. The choice in model physics, VM specification, and use of the aforementioned cost-saving measures enable simulation costs to be low enough such that the cloud can be a viable platform for running short-range ensemble forecasts when compared to the cost of purchasing new computer hardware.
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Eckel, F. Anthony, and Clifford F. Mass. "Aspects of Effective Mesoscale, Short-Range Ensemble Forecasting." Weather and Forecasting 20, no. 3 (June 1, 2005): 328–50. http://dx.doi.org/10.1175/waf843.1.
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Abstract This study developed and evaluated a short-range ensemble forecasting (SREF) system with the goal of producing useful, mesoscale forecast probability (FP). Real-time, 0–48-h SREF predictions were produced and analyzed for 129 cases over the Pacific Northwest. Eight analyses from different operational forecast centers were used as initial conditions for running the fifth-generation Pennsylvania State University–National Center for Atmospheric Research (PSU–NCAR) Mesoscale Model (MM5). Model error is a large source of forecast uncertainty and must be accounted for to maximize SREF utility, particularly for mesoscale, sensible weather phenomena. Although inclusion of model diversity improved FP skill (both reliability and resolution) and increased dispersion toward statistical consistency, dispersion remained inadequate. Conversely, systematic model errors (i.e., biases) must be removed from an SREF since they contribute to forecast error but not to forecast uncertainty. A grid-based, 2-week, running-mean bias correction was shown to improve FP skill through 1) better reliability by adjusting the ensemble mean toward the mean of the verifying analysis, and 2) better resolution by removing unrepresentative ensemble variance. Comparison of the multimodel (each member uses a unique model) and varied-model (each member uses a unique version of MM5) approaches indicated that the multimodel SREF exhibited greater dispersion and superior performance. It was also found that an ensemble of unequally likely members can be skillful as long as each member occasionally performs well. Finally, smaller grid spacing led to greater ensemble spread as smaller scales of motion were modeled. This study indicates substantial utility in current SREF systems and suggests several avenues for further improvement.