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1
Suthendran, K., and T. Arivoli. "Performance Comparison of Variable Step Size Techniques of Sato and Godard-Based Blind Equalizer." Fluctuation and Noise Letters 14, no. 03 (June 29, 2015): 1550024. http://dx.doi.org/10.1142/s0219477515500248.
Повний текст джерелаАнотація:
The modern digital high speed wireless communication system demands quick convergence rate and low steady state error. The balancing between the demands can be achieved by opting step size. Thus, it is essential to define new algorithms to equalize channels and mitigate noise in communications. It is renowned that time varying step size blind equalization technique can speed up the convergence rate and minimize the misadjustment. This work presents a variable step size (VSS) approach based on godard blind equalization algorithm to resolve the conflict between the convergence rate and precision of the fixed step-size godard algorithm. The results of this projected approach is compared with the existing variable step size sato algorithm for a pulse amplitude modulated (PAM) input symbol.
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Kummerow, Christian D., David L. Randel, Mark Kulie, Nai-Yu Wang, Ralph Ferraro, S. Joseph Munchak, and Veljko Petkovic. "The Evolution of the Goddard Profiling Algorithm to a Fully Parametric Scheme." Journal of Atmospheric and Oceanic Technology 32, no. 12 (December 2015): 2265–80. http://dx.doi.org/10.1175/jtech-d-15-0039.1.
Повний текст джерелаАнотація:
AbstractThe Goddard profiling algorithm has evolved from a pseudoparametric algorithm used in the current TRMM operational product (GPROF 2010) to a fully parametric approach used operationally in the GPM era (GPROF 2014). The fully parametric approach uses a Bayesian inversion for all surface types. The algorithm thus abandons rainfall screening procedures and instead uses the full brightness temperature vector to obtain the most likely precipitation state. This paper offers a complete description of the GPROF 2010 and GPROF 2014 algorithms and assesses the sensitivity of the algorithm to assumptions related to channel uncertainty as well as ancillary data. Uncertainties in precipitation are generally less than 1%–2% for realistic assumptions in channel uncertainties. Consistency among different radiometers is extremely good over oceans. Consistency over land is also good if the diurnal cycle is accounted for by sampling GMI product only at the time of day that different sensors operate. While accounting for only a modest amount of the total precipitation, snow-covered surfaces exhibit differences of up to 25% between sensors traceable to the availability of high-frequency (166 and 183 GHz) channels. In general, comparisons against early versions of GPM’s Ku-band radar precipitation estimates are fairly consistent but absolute differences will be more carefully evaluated once GPROF 2014 is upgraded to use the full GPM-combined radar–radiometer product for its a priori database. The combined algorithm represents a physically constructed database that is consistent with both the GPM radars and the GMI observations, and thus it is the ideal basis for a Bayesian approach that can be extended to an arbitrary passive microwave sensor.
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Biscaro, Thiago S., and Carlos A. Morales. "Continental Passive Microwave-Based Rainfall Estimation Algorithm: Application to the Amazon Basin." Journal of Applied Meteorology and Climatology 47, no. 7 (July 1, 2008): 1962–81. http://dx.doi.org/10.1175/2007jamc1744.1.
Повний текст джерелаАнотація:
Abstract This paper presents a new statistical algorithm to estimate rainfall over the Amazon Basin region using the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI). The algorithm relies on empirical relationships derived for different raining-type systems between coincident measurements of surface rainfall rate and 85-GHz polarization-corrected brightness temperature as observed by the precipitation radar (PR) and TMI on board the TRMM satellite. The scheme includes rain/no-rain area delineation (screening) and system-type classification routines for rain retrieval. The algorithm is validated against independent measurements of the TRMM–PR and S-band dual-polarization Doppler radar (S-Pol) surface rainfall data for two different periods. Moreover, the performance of this rainfall estimation technique is evaluated against well-known methods, namely, the TRMM-2A12 [the Goddard profiling algorithm (GPROF)], the Goddard scattering algorithm (GSCAT), and the National Environmental Satellite, Data, and Information Service (NESDIS) algorithms. The proposed algorithm shows a normalized bias of approximately 23% for both PR and S-Pol ground truth datasets and a mean error of 0.244 mm h−1 (PR) and −0.157 mm h−1 (S-Pol). For rain volume estimates using PR as reference, a correlation coefficient of 0.939 and a normalized bias of 0.039 were found. With respect to rainfall distributions and rain area comparisons, the results showed that the formulation proposed is efficient and compatible with the physics and dynamics of the observed systems over the area of interest. The performance of the other algorithms showed that GSCAT presented low normalized bias for rain areas and rain volume [0.346 (PR) and 0.361 (S-Pol)], and GPROF showed rainfall distribution similar to that of the PR and S-Pol but with a bimodal distribution. Last, the five algorithms were evaluated during the TRMM–Large-Scale Biosphere–Atmosphere Experiment in Amazonia (LBA) 1999 field campaign to verify the precipitation characteristics observed during the easterly and westerly Amazon wind flow regimes. The proposed algorithm presented a cumulative rainfall distribution similar to the observations during the easterly regime, but it underestimated for the westerly period for rainfall rates above 5 mm h−1. NESDIS1 overestimated for both wind regimes but presented the best westerly representation. NESDIS2, GSCAT, and GPROF underestimated in both regimes, but GPROF was closer to the observations during the easterly flow.
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TAO, Wei-Kuo, Stephen LANG, Takamichi IGUCHI, and Yi SONG. "Goddard Latent Heating Retrieval Algorithm for TRMM and GPM." Journal of the Meteorological Society of Japan. Ser. II 100, no. 2 (2022): 293–320. http://dx.doi.org/10.2151/jmsj.2022-015.
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Koshigoe, S., A. Teagle, and C. H. Tsay. "A Rapidly Convergent Adaptive Controller Applied to Suppression of Random Noise Transmission." Journal of Vibration and Acoustics 120, no. 2 (April 1, 1998): 449–54. http://dx.doi.org/10.1115/1.2893850.
Повний текст джерелаАнотація:
In this paper, an efficient rapid convergent control algorithm will be developed and will be compared with other adaptive control algorithms using a plate-cavity system. The plate-cavity system used for these numerical experiments is a test bed of noise suppression technology for expendable launch vehicles. It consists of a flexible plate backed by a rigid cavity. Piezoelectric (PZT) actuators are considered to be bonded on both sides of the plate symmetrically. The plate is bombarded with an amplified random noise signal, and the control system is used to suppress the noise inside the cavity generated by the outside sound source. Other control algorithms included for the comparisons are LMS Widrow’s finite impulse response (FIR) adaptive control algorithm [1], and a modified Godard’s algorithm [2]. Comparisons of the random noise attenuation capability, transient and convergence performance, and computational requirements of each algorithm will he made as the order of the controller and relevant convergence parameters are varied.
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Tao, W. K., T. Iguchi, and S. Lang. "Expanding the Goddard CSH Algorithm for GPM: New Extratropical Retrievals." Journal of Applied Meteorology and Climatology 58, no. 5 (May 2019): 921–46. http://dx.doi.org/10.1175/jamc-d-18-0215.1.
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AbstractThe Goddard convective–stratiform heating (CSH) algorithm has been used to retrieve latent heating (LH) associated with clouds and cloud systems in support of the Tropical Rainfall Measuring Mission and Global Precipitation Measurement (GPM) mission. The CSH algorithm requires the use of a cloud-resolving model to simulate LH profiles to build lookup tables (LUTs). However, the current LUTs in the CSH algorithm are not suitable for retrieving LH profiles at high latitudes or winter conditions that are needed for GPM. The NASA Unified-Weather Research and Forecasting (NU-WRF) Model is used to simulate three eastern continental U.S. (CONUS) synoptic winter and three western coastal/offshore events. The relationship between LH structures (or profiles) and other precipitation properties (radar reflectivity, freezing-level height, echo-top height, maximum dBZ height, vertical dBZ gradient, and surface precipitation rate) is examined, and a new classification system is adopted with varying ranges for each of these precipitation properties to create LUTs representing high latitude/winter conditions. The performance of the new LUTs is examined using a self-consistency check for one CONUS and one West Coast offshore event by comparing LH profiles retrieved from the LUTs using model-simulated precipitation properties with those originally simulated by the model. The results of the self-consistency check validate the new classification and LUTs. The new LUTs provide the foundation for high-latitude retrievals that can then be merged with those from the tropical CSH algorithm to retrieve LH profiles over the entire GPM domain using precipitation properties retrieved from the GPM combined algorithm.
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Choi, Jinho, Iickho Song, and Rae-Hong Park. "Some convergence properties of Godard's quartic algorithm." Signal Processing 56, no. 3 (February 1997): 313–20. http://dx.doi.org/10.1016/s0165-1684(96)00178-8.
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Sanderson, Victoria L., Chris Kidd, and Glenn R. McGregor. "A Comparison of TRMM Microwave Techniques for Detecting the Diurnal Rainfall Cycle." Journal of Hydrometeorology 7, no. 4 (August 1, 2006): 687–704. http://dx.doi.org/10.1175/jhm507.1.
Повний текст джерелаАнотація:
Abstract This paper uses rainfall estimates retrieved from active and passive microwave data to investigate how spatially and temporally dependent algorithm biases affect the monitoring of the diurnal rainfall cycle. Microwave estimates used in this study are from the Tropical Rainfall Measuring Mission (TRMM) and include the precipitation radar (PR) near-surface (2A25), Goddard Profiling (GPROF) (2A12), and PR–TRMM Microwave Imager (TMI) (2B31) rain rates from the version 5 (v5) 3G68 product. A rainfall maximum is observed early evening over land, while oceans generally show a minimum in rainfall during the morning. Comparisons of annual and seasonal mean hourly rain rates and harmonics at both global and regional scales show significant differences between the algorithms. Relative and absolute biases over land vary according to the time of day. Clearly, these retrieval biases need accounting for, either in the physics of the algorithm or through the provision of accurate error estimates, to avoid erroneous climatic signals and the discrediting of satellite rainfall estimations.
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Sharma, V., and V. N. Raj. "Convergence and performance analysis of Godard family and multimodulus algorithms for blind equalization." IEEE Transactions on Signal Processing 53, no. 4 (April 2005): 1520–33. http://dx.doi.org/10.1109/tsp.2005.843725.
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Utsumi, Nobuyuki, F. Joseph Turk, Ziad S. Haddad, Pierre-Emmanuel Kirstetter, and Hyungjun Kim. "Evaluation of Precipitation Vertical Profiles Estimated by GPM-Era Satellite-Based Passive Microwave Retrievals." Journal of Hydrometeorology 22, no. 1 (January 2021): 95–112. http://dx.doi.org/10.1175/jhm-d-20-0160.1.
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AbstractPrecipitation estimation based on passive microwave (MW) observations from low-Earth-orbiting satellites is one of the essential variables for understanding the global climate. However, almost all validation studies for such precipitation estimation have focused only on the surface precipitation rate. This study investigates the vertical precipitation profiles estimated by two passive MW-based retrieval algorithms, i.e., the emissivity principal components (EPC) algorithm and the Goddard profiling algorithm (GPROF). The passive MW-based condensed water content profiles estimated from the Global Precipitation Measurement Microwave Imager (GMI) are validated using the GMI + Dual-Frequency Precipitation Radar combined algorithm as the reference product. It is shown that the EPC generally underestimates the magnitude of the condensed water content profiles, described by the mean condensed water content, by about 20%–50% in the middle-to-high latitudes, while GPROF overestimates it by about 20%–50% in the middle-to-high latitudes and more than 50% in the tropics. Part of the EPC magnitude biases is associated with the representation of the precipitation type (i.e., convective and stratiform) in the retrieval algorithm. This suggests that a separate technique for precipitation type identification would aid in mitigating these biases. In contrast to the magnitude of the profile, the profile shapes are relatively well represented by these two passive MW-based retrievals. The joint analysis between the estimation performances of the vertical profiles and surface precipitation rate shows that the physically reasonable connections between the surface precipitation rate and the associated vertical profiles are achieved to some extent by the passive MW-based algorithms.
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Pfreundschuh, Simon, Paula J. Brown, Christian D. Kummerow, Patrick Eriksson, and Teodor Norrestad. "GPROF-NN: a neural-network-based implementation of the Goddard Profiling Algorithm." Atmospheric Measurement Techniques 15, no. 17 (September 2, 2022): 5033–60. http://dx.doi.org/10.5194/amt-15-5033-2022.
Повний текст джерелаАнотація:
Abstract. The Global Precipitation Measurement (GPM) mission measures global precipitation at a temporal resolution of a few hours to enable close monitoring of the global hydrological cycle. GPM achieves this by combining observations from a spaceborne precipitation radar, a constellation of passive microwave (PMW) sensors, and geostationary satellites. The Goddard Profiling Algorithm (GPROF) is used operationally to retrieve precipitation from all PMW sensors of the GPM constellation. Since the resulting precipitation rates serve as input for many of the level 3 retrieval products, GPROF constitutes an essential component of the GPM processing pipeline. This study investigates ways to improve GPROF using modern machine learning methods. We present two neural-network-based, probabilistic implementations of GPROF: GPROF-NN 1D, which (just like the current GPROF implementation) processes pixels individually, and GPROF-NN 3D, which employs a convolutional neural network to incorporate structural information into the retrieval. The accuracy of the retrievals is evaluated using a test dataset consistent with the data used in the development of the GPROF and GPROF-NN retrievals. This allows for assessing the accuracy of the retrieval method isolated from the representativeness of the training data, which remains a major source of uncertainty in the development of precipitation retrievals. Despite using the same input information as GPROF, the GPROF-NN 1D retrieval improves the accuracy of the retrieved surface precipitation for the GPM Microwave Imager (GMI) from 0.079 to 0.059 mm h−1 in terms of mean absolute error (MAE), from 76.1 % to 69.5 % in terms of symmetric mean absolute percentage error (SMAPE) and from 0.797 to 0.847 in terms of correlation. The improvements for the Microwave Humidity Sounder (MHS) are from 0.085 to 0.061 mm h−1 in terms of MAE, from 81 % to 70.1 % for SMAPE, and from 0.724 to 0.804 in terms of correlation. Comparable improvements are found for the retrieved hydrometeor profiles and their column integrals, as well as the detection of precipitation. Moreover, the ability of the retrievals to resolve small-scale variability is improved by more than 40 % for GMI and 29 % for MHS. The GPROF-NN 3D retrieval further improves the MAE to 0.043 mm h−1; the SMAPE to 48.67 %; and the correlation to 0.897 for GMI and 0.043 mm h−1, 63.42 %, and 0.83 for MHS. Application of the retrievals to GMI observations of Hurricane Harvey shows moderate improvements when compared to co-located GPM-combined and ground-based radar measurements indicating that the improvements at least partially carry over to assessment against independent measurements. Similar retrievals for MHS do not show equally clear improvements, leaving the validation against independent measurements for future investigation. Both GPROF-NN algorithms make use of the same input and output data as the original GPROF algorithm and thus may replace the current implementation in a future update of the GPM processing pipeline. Despite their superior accuracy, the single-core runtime required for the operational processing of an orbit of observations is lower than that of GPROF. The GPROF-NN algorithms promise to be a simple and cost-efficient way to increase the accuracy of the PMW precipitation retrievals of the GPM constellation and thus improve the monitoring of the global hydrological cycle.
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Colarco, Peter R., Santiago Gassó, Changwoo Ahn, Virginie Buchard, Arlindo M. da Silva, and Omar Torres. "Simulation of the Ozone Monitoring Instrument aerosol index using the NASA Goddard Earth Observing System aerosol reanalysis products." Atmospheric Measurement Techniques 10, no. 11 (November 3, 2017): 4121–34. http://dx.doi.org/10.5194/amt-10-4121-2017.
Повний текст джерелаАнотація:
Abstract. We provide an analysis of the commonly used Ozone Monitoring Instrument (OMI) aerosol index (AI) product for qualitative detection of the presence and loading of absorbing aerosols. In our analysis, simulated top-of-atmosphere (TOA) radiances are produced at the OMI footprints from a model atmosphere and aerosol profile provided by the NASA Goddard Earth Observing System (GEOS-5) Modern-Era Retrospective Analysis for Research and Applications aerosol reanalysis (MERRAero). Having established the credibility of the MERRAero simulation of the OMI AI in a previous paper we describe updates in the approach and aerosol optical property assumptions. The OMI TOA radiances are computed in cloud-free conditions from the MERRAero atmospheric state, and the AI is calculated. The simulated TOA radiances are fed to the OMI near-UV aerosol retrieval algorithms (known as OMAERUV) is compared to the MERRAero calculated AI. Two main sources of discrepancy are discussed: one pertaining to the OMI algorithm assumptions of the surface pressure, which are generally different from what the actual surface pressure of an observation is, and the other related to simplifying assumptions in the molecular atmosphere radiative transfer used in the OMI algorithms. Surface pressure assumptions lead to systematic biases in the OMAERUV AI, particularly over the oceans. Simplifications in the molecular radiative transfer lead to biases particularly in regions of topography intermediate to surface pressures of 600 and 1013.25 hPa. Generally, the errors in the OMI AI due to these considerations are less than 0.2 in magnitude, though larger errors are possible, particularly over land. We recommend that future versions of the OMI algorithms use surface pressures from readily available atmospheric analyses combined with high-spatial-resolution topographic maps and include more surface pressure nodal points in their radiative transfer lookup tables.
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Lyster, P. M., J. Guo, T. Clune, and J. W. Larson. "The Computational Complexity and Parallel Scalability of Atmospheric Data Assimilation Algorithms." Journal of Atmospheric and Oceanic Technology 21, no. 11 (November 1, 2004): 1689–700. http://dx.doi.org/10.1175/jtech1636.1.
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Abstract This paper quantifies the computational complexity and parallel scalability of two algorithms for four-dimensional data assimilation (4DDA) at NASA's Global Modeling and Assimilation Office (GMAO). The first, the Goddard Earth Observing System Data Assimilation System (GEOS DAS), uses an atmospheric general circulation model (GCM) and an observation-space-based analysis system, the Physical-Space Statistical Analysis System (PSAS). GEOS DAS is very similar to global meteorological weather forecasting data assimilation systems but is used at NASA for climate research. The second, the Kalman filter, uses a more consistent algorithm to determine the forecast error covariance matrix than does GEOS DAS. For atmospheric assimilation, the gridded dynamical fields typically have more than 106 variables; therefore, the full error covariance matrix may be in excess of a teraword. For the Kalman filter this problem will require petaflop s−1 computing to achieve effective throughput for scientific research.
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Sudradjat, Arief, Nai-Yu Wang, Kaushik Gopalan, and Ralph R. Ferraro. "Prototyping a Generic, Unified Land Surface Classification and Screening Methodology for GPM-Era Microwave Land Precipitation Retrieval Algorithms." Journal of Applied Meteorology and Climatology 50, no. 6 (June 2011): 1200–1211. http://dx.doi.org/10.1175/2010jamc2572.1.
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AbstractA prototype generic, unified land surface classification and screening methodology for Global Precipitation Measurement (GPM)-era microwave land precipitation retrieval algorithms by using ancillary datasets is developed. As an alternative to the current radiometer-determined approach, the new methodology is shown to be promising in improving rain detection by providing better surface-cover-type information. The early prototype new surface screening scheme was applied to the current version of the Goddard profiling algorithm that is used for the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (GPROFV6). It has shown improvements in surface-cover-type classification and hence better precipitation retrieval comparisons with TRMM precipitation radar level-2 (L2) (2A25) data and the Global Precipitation Climatology Project (GPCP) version-2.1 (GPCPV2.1) datasets. The new ancillary data approach removes the current dependency of the screening step on relatively different satellite-specific channels and ensures the comparability and continuity of satellite-based precipitation products from different platforms. This is particularly important for advancing the current state of precipitation retrieval over land and for use in merged rainfall products.
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Lang, Stephen E., and Wei-Kuo Tao. "The Next-Generation Goddard Convective–Stratiform Heating Algorithm: New Tropical and Warm-Season Retrievals for GPM." Journal of Climate 31, no. 15 (August 2018): 5997–6026. http://dx.doi.org/10.1175/jcli-d-17-0224.1.
Повний текст джерелаАнотація:
The Goddard convective–stratiform heating (CSH) algorithm, used to estimate cloud heating in support of the Tropical Rainfall Measuring Mission (TRMM), is upgraded in support of the Global Precipitation Measurement (GPM) mission. The algorithm’s lookup tables (LUTs) are revised using new and additional cloud-resolving model (CRM) simulations from the Goddard Cumulus Ensemble (GCE) model, producing smoother heating patterns that span a wider range of intensities because of the increased sampling and finer GPM product grid. Low-level stratiform cooling rates are reduced in the land LUTs for a given rain intensity because of the rain evaporation correction in the new four-class ice (4ICE) scheme. Additional criteria, namely, echo-top heights and low-level reflectivity gradients, are tested for the selection of heating profiles. Those resulting LUTs show greater and more precise variation in their depth of heating as well as a tendency for stronger cooling and heating rates when low-level dB Z values decrease toward the surface. Comparisons versus TRMM for a 3-month period show much more low-level heating in the GPM retrievals because of increased detection of shallow convection, while upper-level heating patterns remain similar. The use of echo tops and low-level reflectivity gradients greatly reduces midlevel heating from ~2 to 5 km in the mean GPM heating profile, resulting in a more top-heavy profile like TRMM versus a more bottom-heavy profile with much more midlevel heating. Integrated latent heating rates are much better balanced versus surface rainfall for the GPM retrievals using the additional selection criteria with an overall bias of +4.3%.
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Kummerow, Christian, Y. Hong, W. S. Olson, S. Yang, R. F. Adler, J. McCollum, R. Ferraro, G. Petty, D.-B. Shin, and T. T. Wilheit. "The Evolution of the Goddard Profiling Algorithm (GPROF) for Rainfall Estimation from Passive Microwave Sensors." Journal of Applied Meteorology 40, no. 11 (November 2001): 1801–20. http://dx.doi.org/10.1175/1520-0450(2001)040<1801:teotgp>2.0.co;2.
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Smedsmo, Jamie L., Efi Foufoula-Georgiou, Venugopal Vuruputur, Fanyou Kong, and Kelvin Droegemeier. "On the Vertical Structure of Modeled and Observed Deep Convective Storms: Insights for Precipitation Retrieval and Microphysical Parameterization." Journal of Applied Meteorology 44, no. 12 (December 1, 2005): 1866–84. http://dx.doi.org/10.1175/jam2306.1.
Повний текст джерелаАнотація:
Abstract An understanding of the vertical structure of clouds is important for remote sensing of precipitation from space and for the parameterization of cloud microphysics in numerical weather prediction (NWP) models. The representation of cloud hydrometeor profiles in high-resolution NWP models has direct applications in inversion-type precipitation retrieval algorithms [e.g., the Goddard profiling (GPROF) algorithm used for retrieval of precipitation from passive microwave sensors] and in quantitative precipitation forecasting. This study seeks to understand how the vertical structure of hydrometeors (liquid and frozen water droplets in a cloud) produced by high-resolution NWP models with explicit microphysics, henceforth referred to as cloud-resolving models (CRMs), compares to observations. Although direct observations of 3D hydrometeor fields are not available, comparisons of modeled and observed radar echoes can provide some insight into the vertical structure of hydrometeors and, in turn, into the ability of CRMs to produce precipitation structures that are consistent with observations. Significant differences are revealed between the vertical structure of observed and modeled clouds of a severe midlatitude storm over Texas for which the surface precipitation is reasonably well captured. Possible reasons for this discrepancy are presented, and the need for future research is highlighted.
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Ryu, Geun-Hyeok, Byung-Ju Sohn, Christian D. Kummerow, Eun-Kyoung Seo, and Gregory J. Tripoli. "Rain-Rate Characteristics over the Korean Peninsula and Improvement of the Goddard Profiling (GPROF) Database for TMI Rainfall Retrievals." Journal of Applied Meteorology and Climatology 51, no. 4 (April 2012): 786–98. http://dx.doi.org/10.1175/jamc-d-11-094.1.
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AbstractSummer rainfall characteristics over the Korean Peninsula are examined using six years of Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) measurements and surface rain measurements from the densely populated rain gauges spread across South Korea. A comparison of the TMI brightness temperature at 85 GHz with the measured surface rain rate reveals that a significant portion of rainfall over the peninsula occurs at warmer brightness temperatures than would be expected from the Goddard profiling (GPROF) database. By incorporating the locally observed rain characteristics into the GPROF algorithm, efforts are made to test whether locally appropriate hydrometeor profiles may be used to improve the retrieved rainfall. Profiles are obtained by simulating rain cases using the cloud-resolving University of Wisconsin Nonhydrostatic Modeling System (UW-NMS) model and matching the calculated radar reflectivities to TRMM precipitation radar (PR) reflectivities. Selected profiles and the corresponding simulated TMI brightness temperatures (limited in this study to values that are larger than 235 K) are added to the GPROF database to form a modified database that is considered to be more suitable for local application over the Korean Peninsula. The rainfall retrieved from the new database demonstrates that heavy-rainfall events—in particular, those associated with warmer clouds—are better captured by the new algorithm as compared with the official TRMM GPROF version-6 retrievals. The results suggest that a more locally suitable rain retrieval algorithm can be developed if locally representative rain characteristics are included in the GPROF algorithm.
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Zhang, Ruanyu, Christian D. Kummerow, David L. Randel, Paula J. Brown, Wesley Berg, and Zhenzhan Wang. "Tropical Cyclone Rain Retrievals from FY-3B MWRI Brightness Temperatures Using the Goddard Profiling Algorithm (GPROF)." Journal of Atmospheric and Oceanic Technology 36, no. 5 (May 2019): 849–64. http://dx.doi.org/10.1175/jtech-d-18-0167.1.
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AbstractThis study focuses on the tropical cyclone rainfall retrieval using FY-3B Microwave Radiation Imager (MWRI) brightness temperatures (Tbs). The GPROF, a fully parametric approach based on the Bayesian scheme, is adapted for use by the MWRI sensor. The MWRI GPROF algorithm is an ocean-only scheme used to estimate rain rates and hydrometeor vertical profiles. An a priori database is constructed from MWRI simulated Tbs, the GPM Microwave Imager (GMI) and Dual-Frequency Precipitation Radar (DPR) combined data, and ancillary data resulting in about 100 000 rainfall profiles. The performance of MWRI retrievals is consistent with DPR observations, even though MWRI retrievals slightly overestimate low rain rates and underestimate high rain rates. The total bias of MWRI retrievals is less than 13% of the mean rain rate of DPR precipitation. Statistical comparisons over GMI GPROF, GMI Hurricane GPROF (HGPROF), and MWRI GPROF retrievals show MWRI GPROF retrievals are consistent in terms of spatial distribution and rain estimates for TCs compared with the other two estimates. In terms of the global precipitation, the mean rain rates at different distances from best track locations for five TC categories are used to identify substantial differences between mean MWRI and GMI GPROF retrievals. After correcting the biases between MWRI and GMI retrievals, the performance of MWRI retrievals shows slight overestimate for light rain rates while underestimating rain rates near the eyewall for category 4 and 5 only.
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Huang, Xianglei, Xiuhong Chen, Gerald L. Potter, Lazaros Oreopoulos, Jason N. S. Cole, Dongmin Lee, and Norman G. Loeb. "A Global Climatology of Outgoing Longwave Spectral Cloud Radiative Effect and Associated Effective Cloud Properties." Journal of Climate 27, no. 19 (September 24, 2014): 7475–92. http://dx.doi.org/10.1175/jcli-d-13-00663.1.
Повний текст джерелаАнотація:
Abstract Longwave (LW) spectral flux and cloud radiative effect (CRE) are important for understanding the earth’s radiation budget and cloud–radiation interaction. Here, the authors extend their previous algorithms to collocated Atmospheric Infrared Sounder (AIRS) and Cloud and the Earth’s Radiant Energy System (CERES) observations over the entire globe and show that the algorithms yield consistently good performances for measurements over both land and ocean. As a result, the authors are able to derive spectral flux and CRE at 10-cm−1 intervals over the entire LW spectrum from all currently available collocated AIRS and CERES observations. Using this multiyear dataset, they delineate the climatology of spectral CRE, including the far IR, over the entire globe as well as in different climate zones. Furthermore, the authors define two quantities, IR-effective cloud-top height (CTHeff) and cloud amount (CAeff), based on the monthly-mean spectral (or band by band) CRE. Comparisons with cloud fields retrieved by the CERES–Moderate Resolution Imaging Spectroradiometer (MODIS) algorithm indicate that, under many circumstances, the CTHeff and CAeff can be related to the physical retrievals of CTH and CA and thus can enhance understandings of model deficiencies in LW radiation budgets and cloud fields. Using simulations from the GFDL global atmosphere model, version 2 (AM2); NASA’s Goddard Earth Observing System, version 5 (GEOS-5); and Environment Canada’s Canadian Centre for Climate Modelling and Analysis (CCCma) Fourth Generation Canadian Atmospheric General Circulation Model (CanAM4) as case studies, the authors further demonstrate the merits of the CTHeff and CAeff concepts in providing insights on global climate model evaluations that cannot be obtained solely from broadband LW flux and CRE comparisons.
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Li, Lijuan, Baozhang Chen, Yanhu Zhang, Youzheng Zhao, Yue Xian, Guang Xu, Huifang Zhang, and Lifeng Guo. "Retrieval of Daily PM2.5 Concentrations Using Nonlinear Methods: A Case Study of the Beijing–Tianjin–Hebei Region, China." Remote Sensing 10, no. 12 (December 11, 2018): 2006. http://dx.doi.org/10.3390/rs10122006.
Повний текст джерелаАнотація:
Exposure to fine particulate matter (PM2.5) is associated with adverse health impacts on the population. Satellite observations and machine learning algorithms have been applied to improve the accuracy of the prediction of PM2.5 concentrations. In this study, we developed a PM2.5 retrieval approach using machine-learning methods, based on aerosol products from the Moderate Resolution Imaging Spectroradiometer (MODIS) aboard the NASA Earth Observation System (EOS) Terra and Aqua polar-orbiting satellites, near-ground meteorological variables from the NASA Goddard Earth Observing System (GEOS), and ground-based PM2.5 observation data. Four models, which are orthogonal regression (OR), regression tree (Rpart), random forests (RF), and support vector machine (SVM), were tested and compared in the Beijing–Tianjin–Hebei (BTH) region of China in 2015. Aerosol products derived from the Terra and Aqua satellite sensors were also compared. The 10-repeat 5-fold cross-validation (10 × 5 CV) method was subsequently used to evaluate the performance of the different aerosol products and the four models. The results show that the performance of the Aqua dataset was better than that of the Terra dataset, and that the RF algorithm has the best predictive performance (Terra: R = 0.77, RMSE = 43.51 μg/m3; Aqua: R = 0.85, RMSE = 33.90 μg/m3). This study shows promise for predicting the spatiotemporal distribution of PM2.5 using the RF model and Aqua aerosol product with the assistance of PM2.5 site data.
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Sullivan, J. T., T. J. McGee, T. Leblanc, G. K. Sumnicht, and L. W. Twigg. "Optimization of the GSFC TROPOZ DIAL retrieval using synthetic lidar returns and ozonesondes – Part 1: Algorithm validation." Atmospheric Measurement Techniques Discussions 8, no. 4 (April 28, 2015): 4273–305. http://dx.doi.org/10.5194/amtd-8-4273-2015.
Повний текст джерелаАнотація:
Abstract. The main purpose of the NASA Goddard Space Flight Center TROPospheric OZone DIfferential Absorption Lidar (GSFC TROPOZ DIAL) is to measure the vertical distribution of tropospheric ozone for science investigations. Because of the important health and climate impacts of tropospheric ozone, it is imperative to quantify background photochemical and aloft ozone concentrations, especially during air quality episodes. To better characterize tropospheric ozone, the Tropospheric Ozone Lidar Network (TOLNet) has recently been developed, which currently consists of five different ozone DIAL instruments, including the TROPOZ. This paper addresses the necessary procedures to validate the TROPOZ retrieval algorithm and develops a primary standard for retrieval consistency and optimization within TOLNet. This paper is focused on ensuring the TROPOZ and future TOLNet algorithms are properly quantifying ozone concentrations and the following paper will focus on defining a systematic uncertainty analysis standard for all TOLNet instruments. Although this paper is used to optimize the TROPOZ retrieval, the methodology presented may be extended and applied to most other DIAL instruments, even if the atmospheric product of interest is not tropospheric ozone (e.g. temperature or water vapor). The analysis begins by computing synthetic lidar returns from actual TROPOZ lidar return signals in combination with a known ozone profile. From these synthetic signals, it is possible to explicitly determine retrieval algorithm biases from the known profile, thereby identifying any areas that may need refinement for a new operational version of the TROPOZ retrieval algorithm. A new vertical resolution scheme is presented, which was upgraded from a constant vertical resolution to a variable vertical resolution, in order to yield a statistical uncertainty of <10%. The optimized vertical resolution scheme retains the ability to resolve fluctuations in the known ozone profile and now allows near field signals to be more appropriately smoothed. With these revisions, the optimized TROPOZ retrieval algorithm (TROPOZopt) has been effective in retrieving nearly 200 m lower to the surface. Also, as compared to the previous version of the retrieval, the TROPOZopt has reduced the mean profile bias by 3.5% and large reductions in bias (near 15 %) were apparent above 4.5 km. Finally, to ensure the TROPOZopt retrieval algorithm is robust enough to handle actual lidar return signals, a comparison is shown between four nearby ozonesonde measurements. The ozonesondes agree well with the retrieval and are mostly within the TROPOZopt retrieval uncertainty bars (which implies that this exercise was quite successful). A final mean percent difference plot is shown between the TROPOZopt and ozonesondes, which indicates that the new operational retrieval is mostly within 10% of the ozonesonde measurement and no systematic biases are present. The authors believe that this analysis has significantly added to the confidence in the TROPOZ instrument and provides a standard for current and future TOLNet algorithms.
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Bellerby, T. J. "Satellite Rainfall Uncertainty Estimation Using an Artificial Neural Network." Journal of Hydrometeorology 8, no. 6 (December 1, 2007): 1397–412. http://dx.doi.org/10.1175/2007jhm846.1.
Повний текст джерелаАнотація:
Abstract This paper describes a neural network–based approach to estimate the conditional distribution function (cdf) of rainfall with respect to multidimensional satellite-derived input data. The methodology [Conditional Histogram of Precipitation (CHIP)] employs a histogram-based approximation of the cdf. In addition to the conditional expected rainfall rate, it provides conditional probabilities for that rate falling within each of a fixed set of intervals or bins. A test algorithm based on the CHIP approach was calibrated against Goddard profiling algorithm (GPROF) rainfall data for June–August 2002 and then used to produce a 30-min, 0.5° rainfall product from global (60°N–60°S) composite geostationary thermal infrared imagery for June–August 2003. Estimated rainfall rates and conditional probabilities were validated against 2003 GPROF data. The CHIP methodology provides the means to extend existing probabilistic and ensemble satellite rainfall error models, conditioned on a single, scalar, satellite rainfall predictor or upon scalar rainfall-rate outputs, to make full use of multidimensional input data.
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Petty, Grant W., and Ke Li. "Improved Passive Microwave Retrievals of Rain Rate over Land and Ocean. Part I: Algorithm Description." Journal of Atmospheric and Oceanic Technology 30, no. 11 (November 1, 2013): 2493–508. http://dx.doi.org/10.1175/jtech-d-12-00144.1.
Повний текст джерелаАнотація:
Abstract A new approach to passive microwave retrievals of precipitation is described that relies on an objective dimensional reduction procedure to filter, normalize, and decorrelate geophysical background noise while retaining the majority of radiometric information concerning precipitation. The dimensional reduction also sharply increases the effective density of any a priori database used in a Bayesian retrieval scheme. The method is applied to passive microwave data from the Tropical Rainfall Measuring Mission (TRMM), reducing the original nine channels to three “pseudochannels” that are relatively insensitive to most background variations occurring within each of seven surface classes (one ocean plus six land and coast) for which they are defined. These pseudochannels may be used in any retrieval algorithm, including the current standard Goddard profiling algorithm (GPROF), in place of the original channels. The same methods are also under development for the Global Precipitation Measurement (GPM) Core Observatory Microwave Imager (GMI). Starting with the pseudochannel definitions, a new Bayesian algorithm for retrieving the surface rain rate is described. The algorithm uses an a priori database populated with matchups between the TRMM precipitation radar (PR) and the TRMM Microwave Imager (TMI). The explicit goal of the algorithm is to retrieve the PR-derived best estimate of the surface rain rate in portions of the TMI swath not covered by the PR. A unique feature of the new algorithm is that it provides robust posterior Bayesian probabilities of pixel-averaged rain rate exceeding various thresholds. Validation and intercomparison of the new algorithm is the subject of a companion paper.
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Wind, Galina, Arlindo M. da Silva, Kerry G. Meyer, Steven Platnick, and Peter M. Norris. "Analysis of the MODIS above-cloud aerosol retrieval algorithm using MCARS." Geoscientific Model Development 15, no. 1 (January 4, 2022): 1–14. http://dx.doi.org/10.5194/gmd-15-1-2022.
Повний текст джерелаАнотація:
Abstract. The Multi-sensor Cloud and Aerosol Retrieval Simulator (MCARS) presently produces synthetic radiance data from Goddard Earth Observing System version 5 (GEOS-5) model output as if the Moderate Resolution Imaging Spectroradiometer (MODIS) were viewing a combination of atmospheric column inclusive of clouds, aerosols, and a variety of gases and land–ocean surface at a specific location. In this paper we use MCARS to study the MODIS Above-Cloud AEROsol retrieval algorithm (MOD06ACAERO). MOD06ACAERO is presently a regional research algorithm able to retrieve aerosol optical thickness over clouds, in particular absorbing biomass-burning aerosols overlying marine boundary layer clouds in the southeastern Atlantic Ocean. The algorithm's ability to provide aerosol information in cloudy conditions makes it a valuable source of information for modeling and climate studies in an area where current clear-sky-only operational MODIS aerosol retrievals effectively have a data gap between the months of June and October. We use MCARS for a verification and closure study of the MOD06ACAERO algorithm. The purpose of this study is to develop a set of constraints a model developer might use during assimilation of MOD06ACAERO data. Our simulations indicate that the MOD06ACAERO algorithm performs well for marine boundary layer clouds in the SE Atlantic provided some specific screening rules are observed. For the present study, a combination of five simulated MODIS data granules were used for a dataset of 13.5 million samples with known input conditions. When pixel retrieval uncertainty was less than 30 %, optical thickness of the underlying cloud layer was greater than 4, and scattering angle range within the cloud bow was excluded, MOD06ACAERO retrievals agreed with the underlying ground truth (GEOS-5 cloud and aerosol profiles used to generate the synthetic radiances) with a slope of 0.913, offset of 0.06, and RMSE=0.107. When only near-nadir pixels were considered (view zenith angle within ±20∘) the agreement with source data further improved (0.977, 0.051, and 0.096 respectively). Algorithm closure was examined using a single case out of the five used for verification. For closure, the MOD06ACAERO code was modified to use GEOS-5 temperature and moisture profiles as an ancillary. Agreement of MOD06ACAERO retrievals with source data for the closure study had a slope of 0.996 with an offset of −0.007 and RMSE of 0.097 at a pixel uncertainty level of less than 40 %, illustrating the benefits of high-quality ancillary atmospheric data for such retrievals.
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Lewis, Jasper R., James R. Campbell, Ellsworth J. Welton, Sebastian A. Stewart, and Phillip C. Haftings. "Overview of MPLNET Version 3 Cloud Detection." Journal of Atmospheric and Oceanic Technology 33, no. 10 (October 2016): 2113–34. http://dx.doi.org/10.1175/jtech-d-15-0190.1.
Повний текст джерелаАнотація:
AbstractThe National Aeronautics and Space Administration Micro Pulse Lidar Network, version 3, cloud detection algorithm is described and differences relative to the previous version are highlighted. Clouds are identified from normalized level 1 signal profiles using two complementary methods. The first method considers vertical signal derivatives for detecting low-level clouds. The second method, which detects high-level clouds like cirrus, is based on signal uncertainties necessitated by the relatively low signal-to-noise ratio exhibited in the upper troposphere by eye-safe network instruments, especially during daytime. Furthermore, a multitemporal averaging scheme is used to improve cloud detection under conditions of a weak signal-to-noise ratio. Diurnal and seasonal cycles of cloud occurrence frequency based on one year of measurements at the Goddard Space Flight Center (Greenbelt, Maryland) site are compared for the new and previous versions. The largest differences, and perceived improvement, in detection occurs for high clouds (above 5 km, above MSL), which increase in occurrence by over 5%. There is also an increase in the detection of multilayered cloud profiles from 9% to 19%. Macrophysical properties and estimates of cloud optical depth are presented for a transparent cirrus dataset. However, the limit to which the cirrus cloud optical depth could be reliably estimated occurs between 0.5 and 0.8. A comparison using collocated CALIPSO measurements at the Goddard Space Flight Center and Singapore Micro Pulse Lidar Network (MPLNET) sites indicates improvements in cloud occurrence frequencies and layer heights.
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Sun, Xiaoli, James B. Abshire, Anand Ramanathan, Stephan R. Kawa, and Jianping Mao. "Retrieval algorithm for the column CO<sub>2</sub> mixing ratio from pulsed multi-wavelength lidar measurements." Atmospheric Measurement Techniques 14, no. 5 (May 27, 2021): 3909–22. http://dx.doi.org/10.5194/amt-14-3909-2021.
Повний текст джерелаАнотація:
Abstract. The retrieval algorithm for CO2 column mixing ratio from measurements of a pulsed multi-wavelength integrated path differential absorption (IPDA) lidar is described. The lidar samples the shape of the 1572.33 nm CO2 absorption line at multiple wavelengths. The algorithm uses a least-squares fit between the CO2 line shape computed from a layered atmosphere model and that sampled by the lidar. In addition to the column-average CO2 dry-air mole fraction (XCO2), several other parameters are also solved simultaneously from the fit. These include the Doppler shift at the received laser signal wavelength, the product of the surface reflectivity and atmospheric transmission, and a linear trend in the lidar receiver's spectral response. The algorithm can also be used to solve for the average water vapor mixing ratio, which produces a secondary absorption in the wings of the CO2 absorption line under humid conditions. The least-squares fit is linearized about the expected XCO2 value, which allows the use of a standard linear least-squares fitting method and software tools. The standard deviation of the retrieved XCO2 is obtained from the covariance matrix of the fit. The averaging kernel is also provided similarly to that used for passive trace-gas column measurements. Examples are presented of using the algorithm to retrieve XCO2 from measurements of the NASA Goddard airborne CO2 Sounder lidar that were made at constant altitude and during spiral-down profile maneuvers.
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Dinku, Tufa, and Emmanouil N. Anagnostou. "TRMM Calibration of SSM/I Algorithm for Overland Rainfall Estimation." Journal of Applied Meteorology and Climatology 45, no. 6 (June 1, 2006): 875–86. http://dx.doi.org/10.1175/jam2379.1.
Повний текст джерелаАнотація:
Abstract This paper extends the work of Dinku and Anagnostou overland rain retrieval algorithm for use with Special Sensor Microwave Imager (SSM/I) observations. In Dinku and Anagnostou, Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR) rainfall estimates were used to calibrate TRMM Microwave Imager (TMI) retrieval. Regional differences in PR-based TMI calibration were investigated by testing the algorithm over four geographic regions, consisting of Africa, northern South America (containing the Amazon basin), the continental United States, and south Asia. In this paper the performance of Dinku and Anagnostou's technique applied on SSM/I data over three of these regions (Africa, Amazon, and South Asia) is demonstrated. Two approaches are investigated for using PR rainfall products to calibrate the algorithm parameters. In the first approach, TMI channels are remapped to the spatial resolutions of the corresponding SSM/I channels; then, PR is used to calibrate the rain retrieval on the remapped TMI data. In the second approach, the PR-based TMI algorithm calibration is performed at a coarser (0.25°) resolution. To assess the quality of algorithm estimates with respect to PR, rainfall fields derived from Dinku and Anagnostou, applied to SSM/I observations (using parameters determined from both approaches), are compared with matched (within ±15 min of the satellites' overpass time difference) PR surface rain rates. Calibration data come from the wet seasons (January–March) of 2000 and 2001. To assess the quality of the estimates with respect to PR, data from a 5-month period (December–April) of 2002, 2003, and 2004 are used. In comparison with the latest version of the Goddard profiling (GPROF) algorithm rain estimates, the current algorithm shows significant improvements in terms of both bias and random error reduction. The paper also shows that rain estimation based on TMI observations is associated with lower error statistics in comparison with the corresponding SSM/I retrievals.
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Tan, Jackson, Walter A. Petersen, Gottfried Kirchengast, David C. Goodrich, and David B. Wolff. "Evaluation of Global Precipitation Measurement Rainfall Estimates against Three Dense Gauge Networks." Journal of Hydrometeorology 19, no. 3 (March 1, 2018): 517–32. http://dx.doi.org/10.1175/jhm-d-17-0174.1.
Повний текст джерелаАнотація:
Abstract Precipitation profiles from the Global Precipitation Measurement (GPM) Core Observatory Dual-Frequency Precipitation Radar (DPR; Ku and Ka bands) form part of the a priori database used in the Goddard profiling algorithm (GPROF) for retrievals of precipitation from passive microwave sensors, which are in turn used as high-quality precipitation estimates in gridded products. As GPROF performs precipitation retrievals as a function of surface classes, error characteristics may be dependent on surface types. In this study, the authors evaluate the rainfall estimates from DPR Ku as well as GPROF estimates from passive microwave sensors in the GPM constellation. The evaluation is conducted at the level of individual satellite pixels (5–15 km) against three dense networks of rain gauges, located over contrasting land surface types and rainfall regimes, with multiple gauges per satellite pixel and precise accumulation about overpass time to ensure a representative comparison. As expected, it was found that the active retrievals from DPR Ku generally performed better than the passive retrievals from GPROF. However, both retrievals struggle under coastal and semiarid environments. In particular, virga appears to be a serious challenge for both DPR Ku and GPROF. The authors detected the existence of lag due to the time it takes for satellite-observed precipitation to reach the ground, but the precise delay is difficult to quantify. It was also shown that subpixel variability is a contributor to the errors in GPROF. These results can pinpoint deficiencies in precipitation algorithms that may propagate into widely used gridded products.
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PANDA, B. S., and D. PRADHAN. "ACYCLIC MATCHINGS IN SUBCLASSES OF BIPARTITE GRAPHS." Discrete Mathematics, Algorithms and Applications 04, no. 04 (December 2012): 1250050. http://dx.doi.org/10.1142/s1793830912500504.
Повний текст джерелаАнотація:
A set M ⊆ E is called an acyclic matching of a graph G = (V, E) if no two edges in M are adjacent and the subgraph induced by the set of end vertices of the edges of M is acyclic. Given a positive integer k and a graph G = (V, E), the acyclic matching problem is to decide whether G has an acyclic matching of cardinality at least k. Goddard et al. (Discrete Math.293(1–3) (2005) 129–138) introduced the concept of the acyclic matching problem and proved that the acyclic matching problem is NP-complete for general graphs. In this paper, we propose an O(n + m) time algorithm to find a maximum cardinality acyclic matching in a chain graph having n vertices and m edges and obtain an expression for the number of maximum cardinality acyclic matchings in a chain graph. We also propose a dynamic programming-based O(n + m) time algorithm to find a maximum cardinality acyclic matching in a bipartite permutation graph having n vertices and m edges. Finally, we strengthen the complexity result of the acyclic matching problem by showing that this problem remains NP-complete for perfect elimination bipartite graphs.
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Lewis, Jasper R., James R. Campbell, Sebastian A. Stewart, Ivy Tan, Ellsworth J. Welton, and Simone Lolli. "Determining cloud thermodynamic phase from the polarized Micro Pulse Lidar." Atmospheric Measurement Techniques 13, no. 12 (December 18, 2020): 6901–13. http://dx.doi.org/10.5194/amt-13-6901-2020.
Повний текст джерелаАнотація:
Abstract. A method to distinguish cloud thermodynamic phase from polarized Micro Pulse Lidar (MPL) measurements is described. The method employs a simple enumerative approach to classify cloud layers as either liquid water, ice water, or mixed-phase clouds based on the linear volume depolarization ratio and cloud top temperatures derived from Goddard Earth Observing System, version 5 (GEOS-5), assimilated data. Two years of cloud retrievals from the Micro Pulse Lidar Network (MPLNET) site in Greenbelt, MD, are used to evaluate the performance of the algorithm. The fraction of supercooled liquid water in the mixed-phase temperature regime (−37–0 ∘C) calculated using MPLNET data is compared to similar calculations made using the spaceborne Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument onboard the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) satellite, with reasonable consistency.
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Arias, E. F., and M. Feissel. "The Celestial System of the International Earth Rotation Service." Symposium - International Astronomical Union 141 (1990): 119–28. http://dx.doi.org/10.1017/s0074180900086472.
Повний текст джерелаАнотація:
The celestial system of the International Earth Rotation Service (IERS) is materialized by the J2000.0 positions of more than 250 extragalactic compact radio sources observed by VLBI. The source coordinates are evaluated from the combination of individual celestial frames obtained by the Goddard Space Flight Center, the Jet Propulsion Laboratory and the U.S. National Geodetic Survey.The combination model and the maintenance algorithm are described. To free the IERS celestial frame from inconsistencies due to the inaccuracy of the IAU conventional models for precession and nutation, it is implemented on individual frames which have been obtained in parallel to the adjustment of corrections to the direction of the celestial pole.The IERS celestial reference frame is consistent with FK5 at a few milliarcsecond level. To be made denser and more accessible for astronomical uses, it will be related to the HIPPARCOS stellar frame.
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Farahmand, A., and A. AghaKouchak. "A satellite-based global landslide model." Natural Hazards and Earth System Sciences 13, no. 5 (May 16, 2013): 1259–67. http://dx.doi.org/10.5194/nhess-13-1259-2013.
Повний текст джерелаАнотація:
Abstract. Landslides are devastating phenomena that cause huge damage around the world. This paper presents a quasi-global landslide model derived using satellite precipitation data, land-use land cover maps, and 250 m topography information. This suggested landslide model is based on the Support Vector Machines (SVM), a machine learning algorithm. The National Aeronautics and Space Administration (NASA) Goddard Space Flight Center (GSFC) landslide inventory data is used as observations and reference data. In all, 70% of the data are used for model development and training, whereas 30% are used for validation and verification. The results of 100 random subsamples of available landslide observations revealed that the suggested landslide model can predict historical landslides reliably. The average error of 100 iterations of landslide prediction is estimated to be approximately 7%, while approximately 2% false landslide events are observed.
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Shi, J. J., W.-K. Tao, T. Matsui, R. Cifelli, A. Hou, S. Lang, A. Tokay, et al. "WRF Simulations of the 20–22 January 2007 Snow Events over Eastern Canada: Comparison with In Situ and Satellite Observations." Journal of Applied Meteorology and Climatology 49, no. 11 (November 1, 2010): 2246–66. http://dx.doi.org/10.1175/2010jamc2282.1.
Повний текст джерелаАнотація:
Abstract One of the grand challenges of the Global Precipitation Measurement (GPM) mission is to improve cold-season precipitation measurements in mid- and high latitudes through the use of high-frequency passive microwave radiometry. For this purpose, the Weather Research and Forecasting model (WRF) with the Goddard microphysics scheme is coupled with a Satellite Data Simulation Unit (WRF–SDSU) to facilitate snowfall retrieval algorithms over land by providing a virtual cloud library and corresponding microwave brightness temperature measurements consistent with the GPM Microwave Imager (GMI). When this study was initiated, there were no prior published results using WRF at cloud-resolving resolution (1 km or finer) for high-latitude snow events. This study tested the Goddard cloud microphysics scheme in WRF for two different snowstorm events (a lake-effect event and a synoptic event between 20 and 22 January 2007) that took place over the Canadian CloudSat/Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) Validation Project (C3VP) site in Ontario, Canada. The 24-h-accumulated snowfall predicted by WRF with the Goddard microphysics was comparable to that observed by the ground-based radar for both events. The model correctly predicted the onset and termination of both snow events at the Centre for Atmospheric Research Experiments site. The WRF simulations captured the basic cloud patterns as seen by the ground-based radar and satellite [i.e., CloudSat and Advanced Microwave Sounding Unit B (AMSU-B)] observations, including the snowband featured in the lake event. The results reveal that WRF was able to capture the cloud macrostructure reasonably well. Sensitivity tests utilizing both the “2ICE” (ice and snow) and “3ICE” (ice, snow, and graupel) options in the Goddard microphysical scheme were also conducted. The domain- and time-averaged cloud species profiles from the WRF simulations with both microphysical options show identical results (due to weak vertical velocities and therefore the absence of large precipitating liquid or high-density ice particles like graupel). Both microphysics options produced an appreciable amount of liquid water, and the model cloud liquid water profiles compared well to the in situ C3VP aircraft measurements when only grid points in the vicinity of the flight paths were considered. However, statistical comparisons between observed and simulated radar echoes show that the model tended to have a high bias of several reflectivity decibels (dBZ), which shows that additional research is needed to improve the current cloud microphysics scheme for the extremely cold environment in high latitudes, despite the fact that the simulated ice/liquid water contents may have been reasonable for both events. Future aircraft observations are also needed to verify the existence of graupel in high-latitude continental snow events.
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You, Yalei, Nai-Yu Wang, Takuji Kubota, Kazumasa Aonashi, Shoichi Shige, Misako Kachi, Christian Kummerow, et al. "Comparison of TRMM Microwave Imager Rainfall Datasets from NASA and JAXA." Journal of Hydrometeorology 21, no. 3 (March 2020): 377–97. http://dx.doi.org/10.1175/jhm-d-19-0022.1.
Повний текст джерелаАнотація:
AbstractThis study compares three TMI rainfall datasets generated by two versions of NASA’s Goddard Profiling algorithm (GPROF2010 and GPROF2017) and JAXA’s Global Satellite Mapping of Precipitation algorithm (GSMaP) over land, coast, and ocean. We use TRMM precipitation radar observations as the reference, and also include CloudSat cloud profiling radar (CPR) observations as the reference over ocean. First, the dynamic thresholds for rainfall detection used by GSMaP and GPROF2017 have better detection capability, indicating by larger Heidke skill score (HSS) values, compared with GPROF2010 over both land and coast. Over ocean, all three datasets have very similar HSS regardless of including CPR observations. Next, intensity analysis shows that no single dataset performs the best according to all three statistical metrics (correlation, root-mean-square error, and relative bias), except that GSMaP performs the best for stratiform precipitation over coast, and GPROF2017 performs the best for convective precipitation over ocean, based on all three metrics. Finally, an error decomposition analysis shows that the total error and its three components have very different characteristics over several regions among these three datasets. For example, the positive total error in GPROF2010 and GSMaP is primarily caused by the positive hit bias over central Africa, while the false bias in GPROF2017 is largely responsible for this positive total error. For future algorithm development, results from this study imply that a convective–stratiform separation technique may be necessary to reduce the large underestimation for convective rain intensity.
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Nowottnick, E. P., P. R. Colarco, E. J. Welton, and A. da Silva. "Use of the CALIOP vertical feature mask for evaluating global aerosol models." Atmospheric Measurement Techniques 8, no. 9 (September 9, 2015): 3647–69. http://dx.doi.org/10.5194/amt-8-3647-2015.
Повний текст джерелаАнотація:
Abstract. We use observations from the space-based Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) to evaluate global aerosol distributions simulated in the NASA Modern Era Retrospective Analysis for Research and Applications aerosol reanalysis (MERRAero). We focus particularly on an evaluation of aerosol types, using the CALIOP vertical feature mask (VFM) algorithm, and look especially at Saharan dust distributions during July 2009. MERRAero consists of an aerosol simulation produced in the Goddard Earth Observing System version 5 (GEOS-5) Earth system model and incorporates assimilation of MODIS-derived aerosol optical thickness (AOT) to constrain column aerosol loadings. For comparison to the CALIOP VFM we construct two synthetic VFMs using the MERRAero aerosol distributions: a CALIOP-like VFM in which we simulate the total attenuated backscatter and particle depolarization ratio from the MERRAero output and pass those into the CALIOP VFM typing algorithm (MERRAero-CALIOP), and an extinction-based VFM in which we use the MERRAero-simulated species-resolved extinction to map the MERRAero species to the CALIOP VFM types (MERRAero-Extinction). By comparing the MERRAero-CALIOP VFM to CALIOP VFM, we can diagnose the aerosol transport and speciation in MERRAero. By comparing the MERRAero-CALIOP and MERRAero-Extinction-simulated VFM, we perform a simple observing system experiment (OSE), which is useful for identifying limitations of the CALIOP VFM algorithm itself. We find that, despite having our column AOT constrained by MODIS, comparison to the CALIOP VFM reveals a greater occurrence of dusty aerosol layers in our MERRAero-CALIOP VFM due to errors in MERRAero aerosol speciation. Additionally, we find that the CALIOP VFM algorithm is challenged when classifying aerosol features when multiple aerosol types are present, as our application of the CALIOP VFM algorithm to MERRAero aerosol distributions classified marine-dominated aerosol layers with low aerosol loadings as polluted dust when the contribution of dust to the total extinction was low.
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Hilburn, K. A., and F. J. Wentz. "Intercalibrated Passive Microwave Rain Products from the Unified Microwave Ocean Retrieval Algorithm (UMORA)." Journal of Applied Meteorology and Climatology 47, no. 3 (March 1, 2008): 778–94. http://dx.doi.org/10.1175/2007jamc1635.1.
Повний текст джерелаАнотація:
Abstract The Unified Microwave Ocean Retrieval Algorithm (UMORA) simultaneously retrieves sea surface temperature, surface wind speed, columnar water vapor, columnar cloud water, and surface rain rate from a variety of passive microwave radiometers including the Special Sensor Microwave Imager (SSM/I), the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI), and the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E). The rain component of UMORA explicitly parameterizes the three physical processes governing passive microwave rain retrievals: the beamfilling effect, cloud and rainwater partitioning, and effective rain layer thickness. Rain retrievals from the previous version of UMORA disagreed among different sensors and were too high in the tropics. These issues have been fixed with more realistic rain column heights and proper modeling of saturation and footprint-resolution effects in the beamfilling correction. The purpose of this paper is to describe the rain algorithm and its recent improvements and to compare UMORA retrievals with Goddard Profiling Algorithm (GPROF) and Global Precipitation Climatology Project (GPCP) rain rates. On average, TMI retrievals from UMORA agree well with GPROF; however, large differences become apparent when the instantaneous retrievals are compared on a pixel-to-pixel basis. The differences are due to fundamental algorithm differences. For example, UMORA generally retrieves higher total liquid water, but GPROF retrieves a higher surface rain rate for a given amount of total liquid water because of differences in microphysical assumptions. Comparison of UMORA SSM/I retrievals with GPCP shows similar spatial patterns, but GPCP has higher global averages because of greater amounts of precipitation in the extratropics. UMORA and GPCP have similar linear trends over the period 1988–2005 with similar spatial patterns.
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Guilloteau, Clément, Efi Foufoula-Georgiou, and Christian D. Kummerow. "Global Multiscale Evaluation of Satellite Passive Microwave Retrieval of Precipitation during the TRMM and GPM Eras: Effective Resolution and Regional Diagnostics for Future Algorithm Development." Journal of Hydrometeorology 18, no. 11 (November 1, 2017): 3051–70. http://dx.doi.org/10.1175/jhm-d-17-0087.1.
Повний текст джерелаАнотація:
Abstract The constellation of spaceborne passive microwave (MW) sensors, coordinated under the framework of the Precipitation Measurement Missions international agreement, continuously produces observations of clouds and precipitation all over the globe. The Goddard profiling algorithm (GPROF) is designed to infer the instantaneous surface precipitation rate from the measured MW radiances. The last version of the algorithm (GPROF-2014)—the product of more than 20 years of algorithmic development, validation, and improvement—is currently used to estimate precipitation rates from the microwave imager GMI on board the GPM core satellite. The previous version of the algorithm (GPROF-2010) was used with the microwave imager TMI on board TRMM. In this paper, TMI-GPROF-2010 estimates and GMI-GPROF-2014 estimates are compared with coincident active measurements from the Precipitation Radar on board TRMM and the Dual-Frequency Precipitation Radar on board GPM, considered as reference products. The objective is to assess the improvement of the GPM-era microwave estimates relative to the TRMM-era estimates and diagnose regions where continuous improvement is needed. The assessment is oriented toward estimating the “effective resolution” of the MW estimates, that is, the finest scale at which the retrieval is able to accurately reproduce the spatial variability of precipitation. A wavelet-based multiscale decomposition of the radar and passive microwave precipitation fields is used to formally define and assess the effective resolution. It is found that the GPM-era MW retrieval can resolve finer-scale spatial variability over oceans than the TRMM-era retrieval. Over land, significant challenges exist, and this analysis provides useful diagnostics and a benchmark against which future retrieval algorithm improvement can be assessed.
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Sullivan, J. T., T. J. McGee, T. Leblanc, G. K. Sumnicht, and L. W. Twigg. "Optimization of the GSFC TROPOZ DIAL retrieval using synthetic lidar returns and ozonesondes – Part 1: Algorithm validation." Atmospheric Measurement Techniques 8, no. 10 (October 9, 2015): 4133–43. http://dx.doi.org/10.5194/amt-8-4133-2015.
Повний текст джерелаАнотація:
Abstract. The main purpose of the NASA Goddard Space Flight Center TROPospheric OZone DIfferential Absorption Lidar (GSFC TROPOZ DIAL) is to measure the vertical distribution of tropospheric ozone for science investigations. Because of the important health and climate impacts of tropospheric ozone, it is imperative to quantify background photochemical ozone concentrations and ozone layers aloft, especially during air quality episodes. For these reasons, this paper addresses the necessary procedures to validate the TROPOZ retrieval algorithm and confirm that it is properly representing ozone concentrations. This paper is focused on ensuring the TROPOZ algorithm is properly quantifying ozone concentrations, and a following paper will focus on a systematic uncertainty analysis. This methodology begins by simulating synthetic lidar returns from actual TROPOZ lidar return signals in combination with a known ozone profile. From these synthetic signals, it is possible to explicitly determine retrieval algorithm biases from the known profile. This was then systematically performed to identify any areas that need refinement for a new operational version of the TROPOZ retrieval algorithm. One immediate outcome of this exercise was that a bin registration error in the correction for detector saturation within the original retrieval was discovered and was subsequently corrected for. Another noticeable outcome was that the vertical smoothing in the retrieval algorithm was upgraded from a constant vertical resolution to a variable vertical resolution to yield a statistical uncertainty of <10 %. This new and optimized vertical-resolution scheme retains the ability to resolve fluctuations in the known ozone profile, but it now allows near-field signals to be more appropriately smoothed. With these revisions to the previous TROPOZ retrieval, the optimized TROPOZ retrieval algorithm (TROPOZopt) has been effective in retrieving nearly 200 m lower to the surface. Also, as compared to the previous version of the retrieval, the TROPOZopt had an overall mean improvement of 3.5 %, and large improvements (upwards of 10–15 % as compared to the previous algorithm) were apparent between 4.5 and 9 km. Finally, to ensure the TROPOZopt retrieval algorithm is robust enough to handle actual lidar return signals, a comparison is shown between four nearby ozonesonde measurements. The ozonesondes are mostly within the TROPOZopt retrieval uncertainty bars, which implies that this exercise was quite successful.
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Yorks, John E., Matthew J. McGill, V. Stanley Scott, Shane W. Wake, Andrew Kupchock, Dennis L. Hlavka, William D. Hart, and Patrick A. Selmer. "The Airborne Cloud–Aerosol Transport System: Overview and Description of the Instrument and Retrieval Algorithms." Journal of Atmospheric and Oceanic Technology 31, no. 11 (November 2014): 2482–97. http://dx.doi.org/10.1175/jtech-d-14-00044.1.
Повний текст джерелаАнотація:
AbstractThe Airborne Cloud–Aerosol Transport System (ACATS) is a Doppler wind lidar system that has recently been developed for atmospheric science capabilities at the NASA Goddard Space Flight Center (GSFC). ACATS is also a high-spectral-resolution lidar (HSRL), uniquely capable of directly resolving backscatter and extinction properties of a particle from a high-altitude aircraft. Thus, ACATS simultaneously measures optical properties and motion of cloud and aerosol layers. ACATS has flown on the NASA ER-2 during test flights over California in June 2012 and science flights during the Wallops Airborne Vegetation Experiment (WAVE) in September 2012. This paper provides an overview of the ACATS method and instrument design, describes the ACATS HSRL retrieval algorithms for cloud and aerosol properties, and demonstrates the data products that will be derived from the ACATS data using initial results from the WAVE project. The HSRL retrieval algorithms developed for ACATS have direct application to future spaceborne missions, such as the Cloud–Aerosol Transport System (CATS) to be installed on the International Space Station (ISS). Furthermore, the direct extinction and particle wind velocity retrieved from the ACATS data can be used for science applications such as dust or smoke transport and convective outflow in anvil cirrus clouds.
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Wind, Galina, Arlindo M. da Silva, Peter M. Norris, Steven Platnick, Shana Mattoo, and Robert C. Levy. "Multi-sensor cloud and aerosol retrieval simulator and remote sensing from model parameters – Part 2: Aerosols." Geoscientific Model Development 9, no. 7 (July 12, 2016): 2377–89. http://dx.doi.org/10.5194/gmd-9-2377-2016.
Повний текст джерелаАнотація:
Abstract. The Multi-sensor Cloud Retrieval Simulator (MCRS) produces a “simulated radiance” product from any high-resolution general circulation model with interactive aerosol as if a specific sensor such as the Moderate Resolution Imaging Spectroradiometer (MODIS) were viewing a combination of the atmospheric column and land–ocean surface at a specific location. Previously the MCRS code only included contributions from atmosphere and clouds in its radiance calculations and did not incorporate properties of aerosols. In this paper we added a new aerosol properties module to the MCRS code that allows users to insert a mixture of up to 15 different aerosol species in any of 36 vertical layers.This new MCRS code is now known as MCARS (Multi-sensor Cloud and Aerosol Retrieval Simulator). Inclusion of an aerosol module into MCARS not only allows for extensive, tightly controlled testing of various aspects of satellite operational cloud and aerosol properties retrieval algorithms, but also provides a platform for comparing cloud and aerosol models against satellite measurements. This kind of two-way platform can improve the efficacy of model parameterizations of measured satellite radiances, allowing the assessment of model skill consistently with the retrieval algorithm. The MCARS code provides dynamic controls for appearance of cloud and aerosol layers. Thereby detailed quantitative studies of the impacts of various atmospheric components can be controlled.In this paper we illustrate the operation of MCARS by deriving simulated radiances from various data field output by the Goddard Earth Observing System version 5 (GEOS-5) model. The model aerosol fields are prepared for translation to simulated radiance using the same model subgrid variability parameterizations as are used for cloud and atmospheric properties profiles, namely the ICA technique. After MCARS computes modeled sensor radiances equivalent to their observed counterparts, these radiances are presented as input to operational remote-sensing algorithms.Specifically, the MCARS-computed radiances are input into the processing chain used to produce the MODIS Data Collection 6 aerosol product (M{O/Y}D04). The M{O/Y}D04 product is of course normally produced from M{O/Y}D021KM MODIS Level-1B radiance product directly acquired by the MODIS instrument. MCARS matches the format and metadata of a M{O/Y}D021KM product. The resulting MCARS output can be directly provided to MODAPS (MODIS Adaptive Processing System) as input to various operational atmospheric retrieval algorithms. Thus the operational algorithms can be tested directly without needing to make any software changes to accommodate an alternative input source.We show direct application of this synthetic product in analysis of the performance of the MOD04 operational algorithm. We use biomass-burning case studies over Amazonia employed in a recent Working Group on Numerical Experimentation (WGNE)-sponsored study of aerosol impacts on numerical weather prediction (Freitas et al., 2015). We demonstrate that a known low bias in retrieved MODIS aerosol optical depth appears to be due to a disconnect between actual column relative humidity and the value assumed by the MODIS aerosol product.
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Meyers, Patrick C., Ralph R. Ferraro, and Nai-Yu Wang. "Updated Screening Procedures for GPROF2010 over Land: Utilization for AMSR-E." Journal of Atmospheric and Oceanic Technology 32, no. 5 (May 2015): 1015–28. http://dx.doi.org/10.1175/jtech-d-14-00149.1.
Повний текст джерелаАнотація:
AbstractThe Goddard profiling algorithm 2010 (GPROF2010) was revised for the Advanced Microwave Scanning Radiometer for Earth Observing System (EOS; AMSR-E) instrument. The GPROF2010 land algorithm was developed for the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI), which observes slightly different central frequencies than AMSR-E. A linear transfer function was developed to convert AMSR-E brightness temperatures to their corresponding TMI frequency for raining and nonraining instantaneous fields of view (IFOVs) using collocated brightness temperature and TRMM precipitation radar (PR) measurements. Previous versions of the algorithm separated rain from surface ice, snow, and desert using a series of empirical procedures. These occasionally failed to separate raining and nonraining scenes, leading to failed detection and false alarms of rain. The new GPROF2010, version 2 (GPROF2010V2), presented here, prefaced the heritage screening procedures by referencing annual desert and monthly snow climatologies to identify IFOVs where rain retrievals were unreliable. Over a decade of satellite- and ground-based observations from the Interactive Multisensor Snow and Ice Mapping System (IMS) and AMSR-E allowed for the creation of a medium-resolution (0.25° × 0.25°) climatology of monthly snow and ice cover. The scattering signature of rain over ice and snow is not well defined because of complex emissivity signals dependent on snow depth, age, and melting, such that using a static climatology was a more stable approach to defining surface types. GPROF2010V2 was subsequently used for the precipitation environmental data record (EDR) for the AMSR2 sensor aboard the Global Change Observation Mission–Water 1 (GCOM-W1).
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Hou, Arthur Y., and Sara Q. Zhang. "Assimilation of Precipitation Information Using Column Model Physics as a Weak Constraint." Journal of the Atmospheric Sciences 64, no. 11 (November 1, 2007): 3865–78. http://dx.doi.org/10.1175/2006jas2028.1.
Повний текст джерелаАнотація:
Abstract Currently, operational weather forecasting systems use observations to optimize the initial state of a forecast without considering possible model deficiencies. For precipitation assimilation, this could be an issue since precipitation observations, unlike conventional data, do not directly provide information on the atmospheric state but are related to the state variables through parameterized moist physics with simplifying assumptions. Precipitation observation operators are comparatively less accurate than those for conventional data or observables in clear-sky regions, which can limit data usage not because of issues with observations, but with the model. The challenge lies in exploring new ways to make effective use of precipitation data in the presence of model errors. This study continues the investigation of variational algorithms for precipitation assimilation using column model physics as a weak constraint. The strategy is to develop techniques to make online estimation and correction of model errors to improve the precipitation observation operator during the assimilation cycle. Earlier studies have shown that variational continuous assimilation (VCA) of tropical rainfall using moisture tendency correction can improve Goddard Earth Observing System 3 (GEOS-3) global analyses and forecasts. Here results are presented from a 4-yr GEOS-3 reanalysis assimilating Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) and Special Sensor Microwave Imager (SSM/I) tropical rainfall using the VCA scheme. Comparisons with NCEP operational analysis and the 40-yr ECMWF Re-Analysis (ERA-40) show that the GEOS-3 reanalysis is significantly better at replicating the intensity and variability of tropical precipitation systems ranging from a few days to interannual time scales. As a further refinement of rainfall assimilation using the VCA scheme, a variational algorithm for assimilating TMI latent heating retrievals using semiempirical parameters in the model moist physics as control variables is described and initial test results are presented.
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Nowottnick, E. P., P. R. Colarco, E. J. Welton, and A. da Silva. "Use of the CALIOP vertical feature mask for evaluating global aerosol models." Atmospheric Measurement Techniques Discussions 8, no. 1 (January 30, 2015): 1401–55. http://dx.doi.org/10.5194/amtd-8-1401-2015.
Повний текст джерелаАнотація:
Abstract. Global aerosol distributions provided by the NASA Modern Era Retrospective Analysis for Research and Applications aerosol reanalysis (MERRAero) are evaluated using the aerosol types identified by the CALIOP vertical feature mask (VFM) algorithm, focusing especially on Saharan dust distributions during July 2009. MERRAero is comprised of an aerosol simulation produced in the Goddard Earth Observing System version 5 (GEOS-5) Earth system model and incorporates assimilation of MODIS-derived aerosol optical thickness to constrain column aerosol loadings. For comparison to the CALIOP VFM we construct two synthetic VFMs using the MERRAero aerosol distributions: a Level 2 VFM in which simulated MERRAero total attenuated backscatter and estimated particulate depolarization ratios are input directly to the CALIOP VFM typing algorithm, and a Level 3 VFM in which we map the aerosol species in MERRAero to the CALIOP VFM types. By comparing the simulated MERRAero-Level 2 VFM to CALIOP VFM we can diagnose the aerosol transport and speciation in MERRAero. By comparing the MERRAero-Level 2 and MERRAero-Level 3 simulated VFMs we perform a simple Observing System Simulation Experiment (OSSE), which is useful for identifying shortcomings in the CALIOP VFM algorithm itself. We find that despite having our column AOT constrained by MODIS, comparison to the CALIOP VFM reveals a greater occurrence of dusty aerosol layers in our MERRAero-Level 2 VFM, due to errors in MERRAero aerosol speciation. Additionally, we find that the CALIOP VFM algorithm classification for desert dust and polluted dust should be reconsidered for aerosol features that contain dust mixtures in low aerosol loadings, as our application of the CALIOP VFM to MERRAero distributions flagged a greater presence of dusty vs. marine aerosols when our two MERRAero VFMs were compared.
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Torricella, F., E. Cattani, and V. Levizzani. "Exploitation of cloud top characterization from three-channel IR measurements in a physical PMW rain retrieval algorithm." Advances in Geosciences 7 (January 13, 2006): 19–23. http://dx.doi.org/10.5194/adgeo-7-19-2006.
Повний текст джерелаАнотація:
Abstract. Rainfall intensity estimates by passive microwave (PMW) measurements from space perform generally better over the sea surface with respect to land, due to the problems in separating true rain signatures from those produced by surfaces having similar spectral behaviour (e.g. snow, ice, desert and semiarid grounds). The screening procedure aimed at recognizing the various surface types and delimit precipitation is based on tests that rely on PMW measurements only and global thresholds. The shortcoming is that the approach tries to discard spurious precipitating features (often detected over the land-sea border) thus leading to no-rain conservative tests and thresholds. The TRMM mission, with its long record of simultaneous data from the Visible and Infrared Radiometer System (VIRS), the TRMM Microwave Imager (TMI) and rain profiles from the Precipitation Radar (PR) allows for unambiguous testing of the usefulness of cloud top characterization in rain detection. An intense precipitation event over the North Africa is analysed exploiting a night microphysical RGB scheme applied to VIRS measurements to classify and characterize the components of the observed scenario and to discriminate the various types of clouds. This classification is compared to the rain intensity maps derived from TMI by means of the Goddard profiling algorithm and to the near-surface rain intensities derived from PR. The comparison allows to quantify the difference between the two rain retrievals and to assess the usefulness of RGB analysis in identifying areas of precipitation.
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Elsaesser, Gregory S., and Christian D. Kummerow. "The Sensitivity of Rainfall Estimation to Error Assumptions in a Bayesian Passive Microwave Retrieval Algorithm." Journal of Applied Meteorology and Climatology 54, no. 2 (February 2015): 408–22. http://dx.doi.org/10.1175/jamc-d-14-0105.1.
Повний текст джерелаАнотація:
AbstractThe Goddard profiling algorithm (GPROF) uses Bayesian probability theory to retrieve rainfall over the global oceans. A critical component of GPROF and most Bayes theorem–based retrieval frameworks is the specification of uncertainty in the observations being utilized to retrieve the parameter of interest. In the case of GPROF, for any sensor, uncertainties in microwave brightness temperatures (Tbs) arise from radiative transfer model errors, satellite sensor noise and/or degradation, and nonlinear, scene-dependent Tb offsets added during sensor intercalibration procedures. All mentioned sources impact sensors in a varying fashion, in part because of sensor-dependent fields of view. It is found that small errors in assumed Tb uncertainty (ranging from 0.57 K at 10 GHz to 2.29 K at 85 GHz) lead to a 3.6% change in the retrieved global-average oceanic rainfall rate, and 10%–20% (20%–40%) shifts in the pixel-level (monthly) frequency distributions for given rainfall bins. A mathematical expression describing the sensitivity of retrieved rainfall to uncertainty is developed here. The strong global sensitivity is linked to rainfall variance scaling systematically as Tb varies. For ocean scenes, the same emission-dominated rainfall–Tb physics used in passive microwave rainfall retrieval is also responsible for the substantial underestimation (overestimation) of global rainfall if uncertainty is overestimated (underestimated). Proper uncertainties are required to quantify variability in surface rainfall, assess long-term trends, and provide robust rainfall benchmarks for general circulation model evaluations. The implications for assessing global and regional biases in active versus passive microwave rainfall products, and for achieving rainfall product agreement among a constellation of orbiting microwave radiometers [employed in the Global Precipitation Measurement (GPM) mission], are also discussed.
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Smith, L. L., and J. C. Gille. "Validation of the Aura High Resolution Dynamics Limb Sounder geopotential heights." Atmospheric Measurement Techniques Discussions 7, no. 2 (February 5, 2014): 1001–25. http://dx.doi.org/10.5194/amtd-7-1001-2014.
Повний текст джерелаАнотація:
Abstract. Global satellite observations from the EOS Aura spacecraft's High Resolution Dynamics Limb Sounder (HIRDLS) of temperature and geopotential height (GPH) are discussed. The accuracy, resolution and precision of the HIRDLS version 7 algorithms are assessed and data screening recommendations are made. Comparisons with GPH from observations, reanalyses and models including European Center for Medium-Range Weather Forecasts Interim Reanalysis (ERA-Interim), National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) Reanalysis, Goddard Earth Observing System Model (GEOS) version 5, and EOS Aura Microwave Limb Sounder (MLS) illustrate the HIRDLS GPH have a precision ranging from 2 m to 30 m and an accuracy of ±100 m. Comparisons indicate HIRDLS GPH may have a slight low bias in the tropics and a slight high bias at high latitudes. Geostrophic winds computed with HIRDLS GPH qualitatively agree with winds from other data sources including ERA-Interim, NCEP and GEOS-5.
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Seto, Shinta, Takuji Kubota, Nobuhiro Takahashi, Toshio Iguchi, and Taikan Oki. "Advanced Rain/No-Rain Classification Methods for Microwave Radiometer Observations over Land." Journal of Applied Meteorology and Climatology 47, no. 11 (November 1, 2008): 3016–29. http://dx.doi.org/10.1175/2008jamc1895.1.
Повний текст джерелаАнотація:
Abstract Seto et al. developed rain/no-rain classification (RNC) methods over land for the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI). In this study, the methods are modified for application to other microwave radiometers. The previous methods match TMI observations with TRMM precipitation radar (PR) observations, classify the TMI pixels into rain pixels and no-rain pixels, and then statistically summarize the observed brightness temperature at the no-rain pixels into a land surface brightness temperature database. In the modified methods, the probability distribution of brightness temperature under no-rain conditions is derived from unclassified TMI pixels without the use of PR. A test with the TMI shows that the modified (PR independent) methods are better than the RNC method developed for the Goddard profiling algorithm (GPROF; the standard algorithm for the TMI) while they are slightly poorer than corresponding previous (PR dependent) methods. M2d, one of the PR-independent methods, is applied to observations from the Advanced Microwave Scanning Radiometer for Earth Observing Satellite (AMSR-E), is evaluated for a matchup case with PR, and is evaluated for 1 yr with a rain gauge dataset in Japan. M2d is incorporated into a retrieval algorithm developed by the Global Satellite Mapping of Precipitation project to be applied for the AMSR-E. In latitudes above 30°N, the rain-rate retrieval is compared with a rain gauge dataset by the Global Precipitation Climatology Center. Without a snow mask, a large amount of false rainfall due to snow contamination occurs. Therefore, a simple snow mask using the 23.8-GHz channel is applied and the threshold of the mask is optimized. Between 30° and 60°N, the optimized snow mask forces the miss of an estimated 10% of the total rainfall.
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Lin, Xin, Sara Q. Zhang, and Arthur Y. Hou. "Variational Assimilation of Global Microwave Rainfall Retrievals: Physical and Dynamical Impact on GEOS Analyses." Monthly Weather Review 135, no. 8 (August 1, 2007): 2931–57. http://dx.doi.org/10.1175/mwr3434.1.
Повний текст джерелаАнотація:
Abstract Global microwave rainfall retrievals from a five-satellite constellation, including the Tropical Rainfall Measuring Mission Microwave Imager, Special Sensor Microwave Imager from the Defense Meteorological Satellite Program F13, F14, and F15, and the Advanced Microwave Scanning Radiometer from the Earth Observing System Aqua, are assimilated into the NASA Goddard Earth Observing System (GEOS) Data Assimilation System using a 1D variational continuous assimilation (VCA) algorithm. The physical and dynamical impact of rainfall assimilation on GEOS analyses is examined at various temporal and spatial scales. This study demonstrates that the 1D VCA algorithm, which was originally developed and evaluated for rainfall assimilations over tropical oceans, can effectively assimilate satellite microwave rainfall retrievals and improve GEOS analyses over both the Tropics and the extratropics where the atmospheric processes are dominated by different large-scale dynamics and moist physics, and also over land, where rainfall estimates from passive microwave radiometers are believed to be less accurate. Results show that rainfall assimilation renders the GEOS analysis physically and dynamically more consistent with the observed precipitation at the monthly mean and 6-h time scales. Over regions where the model precipitation tends to misbehave in distinctly different rainy regimes, the 1D VCA algorithm, by compensating for errors in the model’s moist time tendency in a 6-h analysis window, is able to bring the rainfall analysis closer to the observed. The radiation and cloud fields also tend to be in better agreement with independent satellite observations in the rainfall–assimilation run especially over regions where rainfall analyses indicate large improvements. Assimilation experiments with and without rainfall data for a midlatitude frontal system clearly indicate that the GEOS analysis is improved through changes in the thermodynamic and dynamic fields that respond to the rainfall assimilation. The synoptic structures of temperature, moisture, winds, divergence, and vertical motion, as well as vorticity, are more realistically captured across the front.
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Farhadi, Leila, Rolf H. Reichle, Gabriëlle J. M. De Lannoy, and John S. Kimball. "Assimilation of Freeze–Thaw Observations into the NASA Catchment Land Surface Model." Journal of Hydrometeorology 16, no. 2 (April 1, 2015): 730–43. http://dx.doi.org/10.1175/jhm-d-14-0065.1.
Повний текст джерелаАнотація:
Abstract The land surface freeze–thaw (F/T) state plays a key role in the hydrological and carbon cycles and thus affects water and energy exchanges and vegetation productivity at the land surface. In this study, an F/T assimilation algorithm was developed for the NASA Goddard Earth Observing System, version 5 (GEOS-5), modeling and assimilation framework. The algorithm includes a newly developed observation operator that diagnoses the landscape F/T state in the GEOS-5 Catchment land surface model. The F/T analysis is a rule-based approach that adjusts Catchment model state variables in response to binary F/T observations, while also considering forecast and observation errors. A regional observing system simulation experiment was conducted using synthetically generated F/T observations. The assimilation of perfect (error free) F/T observations reduced the root-mean-square errors (RMSEs) of surface temperature and soil temperature by 0.206° and 0.061°C, respectively, when compared to model estimates (equivalent to a relative RMSE reduction of 6.7% and 3.1%, respectively). For a maximum classification error CEmax of 10% in the synthetic F/T observations, the F/T assimilation reduced the RMSE of surface temperature and soil temperature by 0.178° and 0.036°C, respectively. For CEmax = 20%, the F/T assimilation still reduces the RMSE of model surface temperature estimates by 0.149°C but yields no improvement over the model soil temperature estimates. The F/T assimilation scheme is being developed to exploit planned F/T products from the NASA Soil Moisture Active Passive (SMAP) mission.