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Jeffrey R. McCollum and Ralph R. Ferraro

Abstract

The microwave coastal rain identification procedure that has been used by NASA for over 10 yr, and also more recently by NOAA, for different instruments beginning with the Special Sensor Microwave Imager (SSM/I), is updated for use with Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) and Advanced Microwave Scanning Radiometer (AMSR)-[Earth Observing System (EOS)] E microwave data. Since the development of the SSM/I algorithm, a wealth of both space-based and ground-based radar-rainfall estimates have become available, and here some of these data are used with collocated TMI and AMSR-E data to improve the estimation of coastal rain areas from microwave data. Two major improvements are made. The first involves finding the conditions where positive rain rates should be estimated rather than leaving the areas without estimates as in the previous algorithm. The second is a modification to the final step of the rain identification method; previously, a straight brightness temperature cutoff was used, but this is modified to a polarization-corrected temperature criterion. These modifications are made for the TRMM version 6 product release and the third (1 September) release of AMSR-E products to the public, both in 2004. The modifications are slightly different for each of these two sensors.

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Qihang Li, Ralph Ferraro, and Norman Grody

Abstract

Until recently, monthly rainfall products using the National Oceanic and Atmospheric Administration National Environmental Satellite, Data, and Information Service Office of Research and Applications Special Sensor Microwave Imager (SSM/I) rainfall algorithm have been generated on a global 2.5° × 2.5° grid. The rainfall estimates are based on a subsampled set of the full-resolution SSM/I data, with a resulting spatial density of about one-third of what is possible at SSM/I’s highest spatial resolution. The reduction in the spatial resolution was introduced in 1992 as a compromise dictated by data processing capabilities. Currently, daily SSM/I data processing at full resolution has been established and is being operated in parallel with the subsampled set. Reprocessing of the entire SSM/I time series based on the full-resolution data is plausible but requires the reprocessing of over 10 yr of retrospective data. Because the Global Precipitation Climatology Project is considering the generation of a daily 1° × 1° rainfall product, it is important that the effects of using the reduced spatial resolution be reexamined.

In this study, error due to using the reduced-resolution versus the full-resolution SSM/I data in the gridded products at 2.5° and 1° grid sizes is examined. The estimates are based on statistics from radar-derived rain data and from SSM/I data taken over the Tropical Ocean and Global Atmosphere Coupled Ocean–Atmosphere Response Experiment (TOGA COARE) radar site. SSM/I data at full resolution were assumed to provide rain estimates with 12.5-km spacing. Subsampling with spacings of 25, 37.5 (which corresponds to the present situation of ⅓° latitude–longitude spatial resolution), and 50 km were considered. For the instantaneous 2.5° × 2.5° product, the error due to subsampling, expressed as a percentage of the gridbox mean, was estimated using radar-derived data and was 6%, 10%, and 15% at these successively poorer sampling densities. For monthly averaged products on a 2.5° × 2.5° grid, it was substantially lower: 3%, 4%, and 7%, respectively. Subsampling errors for monthly averages on a 1° × 1° grid were 8%, 16%, and 23%, respectively. Estimates based on SSM/I data at full resolution gave errors that were somewhat larger than those from radar-based estimates. It was concluded that a rain product of monthly averages on a 1° × 1° grid must use the full-resolution SSM/I data. More work is needed to determine how applicable these estimates are to other areas of the globe with substantially different rain statistics.

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Ralph R. Ferraro and Gerard F. Marks

Abstract

Rainfall algorithms developed for the DMSP Special Sensor Microwave/Imager are presented and then “calibrated” against ground-based radar measurements to develop instantaneous rain-rate retrieval algorithms. These include both scattering- and emission-based algorithms. Radar data from Japan, the United States, and the United Kingdom have been used in the investigation. Because of the difficulties in accurately matching the satellite and radar measurements in both time and space, an approach where both measurements are grouped in 1 mm h−1 rain-rate bins provides a much more accurate set of measurements to be used in the derivation of coefficients for instantaneous rain-rate retrieval. Both linear and nonlinear relationships are developed, with the nonlinear fits being more accurate and supported by model simulations. An application of the derived instantaneous rain-rate relationships to an independent case is presented, with approximately a 10% error for the scattering algorithm when compared with radar-derived rain rates.

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Daniel Vila, Ralph Ferraro, and Hilawe Semunegus

Abstract

Global monthly rainfall estimates have been produced from more than 20 years of measurements from the Defense Meteorological Satellite Program series of Special Sensor Microwave Imager (SSM/I). This is the longest passive microwave dataset available to analyze the seasonal, annual, and interannual rainfall variability on a global scale. The primary algorithm used in this study is an 85-GHz scattering-based algorithm over land, while a combined 85-GHz scattering and 19/37-GHz emission is used over ocean. The land portion of this algorithm is one of the components of the blended Global Precipitation Climatology Project rainfall climatology. Because previous SSM/I processing was performed in real time, only a basic quality control (QC) procedure had been employed to avoid unrealistic values in the input data. A more sophisticated, statistical-based QC procedure on the daily data grids (antenna temperature) was developed to remove unrealistic values not detected in the original database and was employed to reprocess the rainfall product using the current version of the algorithm for the period 1992–2007. Discrepancies associated with the SSM/I-derived monthly rainfall products are characterized through comparisons with various gauge-based and other satellite-derived rainfall estimates. A substantial reduction in biases was observed as a result of this QC scheme. This will yield vastly improved global rainfall datasets.

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Arief Sudradjat, Ralph R. Ferraro, and Michael Fiorino

Abstract

This study compares monthly total precipitable water (TPW) from the National Aeronautics and Space Administration (NASA) Water Vapor Project (NVAP) and reanalyses of the National Centers for Environmental Prediction (NCEP) (R-1), NCEP–Department of Energy (DOE) Atmospheric Model Intercomparison Project (AMIP-II) (R-2), and the 40-yr European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA-40) from January 1988 through December 1999. Based on the means, NVAP exhibits systematic wetter land regions relative to the other datasets reflecting differences in their analyses due to paucity in radiosonde observations. ERA-40 is wetter in the atmospheric convergence zones than the U.S. reanalyses and NVAP ranges in between. Differences in the annual cycle between the reanalyses (especially R-2) and NVAP are also noticeable over the tropical oceans. Analyses on the interannual variabilities show that the ENSO-related spatial pattern in ERA-40 follows more coherently that of NVAP than those of the U.S. reanalyses. The 1997/98 El Niño’s effect on TPW is shown to be strongest only in NVAP, R-1, and ERA-40 during the period of study. All the datasets show TPW decreases in the Tropics following the 1991 Mt. Pinatubo eruption. By subtracting SST-estimated TPW from the datasets, only NVAP and ERA-40 can well represent the spatial pattern of convergence and/or moist-air advection zones in the Tropics. Even though all the datasets are viable for water cycle and climate analyses with discrepancies (wetness and dryness) to be aware of, this study has found that NVAP and ERA-40 perform better than the U.S. reanalyses during the 12-yr period.

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Chris Kidd, Ralph Ferraro, and Vincenzo Levizzani

Abstract

No Abstract available.

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Veljko Petković, Marko Orescanin, Pierre Kirstetter, Christian Kummerow, and Ralph Ferraro

Abstract

A decades-long effort in observing precipitation from space has led to continuous improvements of satellite-derived passive microwave (PMW) large-scale precipitation products. However, due to a limited ability to relate observed radiometric signatures to precipitation type (convective and stratiform) and associated precipitation rate variability, PMW retrievals are prone to large systematic errors at instantaneous scales. The present study explores the use of deep learning approach in extracting the information content from PMW observation vectors to help identify precipitation types. A deep learning neural network model (DNN) is developed to retrieve the convective type in precipitating systems from PMW observations. A 12-month period of Global Precipitation Measurement mission Microwave Imager (GMI) observations is used as a dataset for model development and verification. The proposed DNN model is shown to accurately predict precipitation types for 85% of total precipitation volume. The model reduces precipitation rate bias associated with convective and stratiform precipitation in the GPM operational algorithm by a factor of 2 while preserving the correlation with reference precipitation rates, and is insensitive to surface type variability. Based on comparisons against currently used convective schemes, it is concluded that the neural network approach has the potential to address regime-specific PMW satellite precipitation biases affecting GPM operations.

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David S. Crosby, Ralph R. Ferraro, and Helen Wu

Abstract

The SSM/I has been used successfully to estimate precipitation and to determine the fields of view (FOV) that contain precipitating clouds. The use of multivariate logistic regression with the SSM/I brightness temperatures to estimate the probability that it is raining in an FOV is examined. The predictors used in this study are those that have been evaluated by other investigators to estimate rain events using other procedures. The logistic regression technique is applied to a matched set of SSM/I and radar data for a limited area from June to August 1989. For this limited dataset the results are quite good. In one example, if the predicted probability is less than 0.1, the radar data shows only 2 of 340 FOVs have precipitation. If the predicted probability is greater than 0.9, the radar data shows precipitation in 748 of 774 FOVS. These probabilities can be used for both instantaneous and climate timescale retrievals.

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John C. Alishouse, Ralph R. Ferraro, and Joseph V. Fiore

Abstract

This paper presents empirically derived equations for Predicting radar reflectivity and the fraction of the field-of-view of a microwave radiometer that is filled with rain. We compare the horizontally and vertically polarized 37 GHz brightness temperatures from the Nimbus 7 SMMR and with high resolution, high quality coincident radar data. Algorithms for horizontal, vertical, and circular polarization are presented. Our results are compared with other investigators.

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Ralph R. Ferraro, Fuzhong Weng, Norman C. Grody, and Alan Basist

The Special Sensor Microwave/Imager (SSM/I), first placed into operation in July 1987, has been making measurements of earth-emitted radiation for over eight years. These data are used to estimate both atmospheric and surface hydrological parameters and to generate a time series of global monthly mean products averaged to a 1° lat × 1° long grid. Specifically, this includes monthly estimates of rainfall and its frequency, cloud liquid water and cloud frequency, water vapor, snow cover frequency, and sea ice frequency. This study uses seasonal mean values to demonstrate the spatial and temporal distributions of these hydrological variables. Examples of interannual variability such as the 1993 flooding in the Mississippi Valley and the 1992–93 snow cover changes over the United States are used to demonstrate the utility of these data for regional applications.

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