scholarly journals Comparison of the GPCP and CMAP Merged Gauge–Satellite Monthly Precipitation Products for the Period 1979–2001

2004 ◽  
Vol 5 (6) ◽  
pp. 1207-1222 ◽  
Author(s):  
Xungang Yin ◽  
Arnold Gruber ◽  
Phil Arkin
Keyword(s):  
Spatial Correlation ◽  
Input Data ◽  
Sampling Error ◽  
Global Precipitation Climatology Project ◽  
Monthly Precipitation ◽  
Tropical Ocean ◽  
Microwave Sounding Unit ◽  
Microwave Sounding ◽  
Microwave Imager ◽  
Global Precipitation

Abstract The two monthly precipitation products of the Global Precipitation Climatology Project (GPCP) and the Climate Prediction Center (CPC) Merged Analysis of Precipitation (CMAP) are compared on a 23-yr period, January 1979–December 2001. For the long-term mean, major precipitation patterns are clearly demonstrated by both products, but there are differences in the pattern magnitudes. In the tropical ocean the CMAP is higher than the GPCP, but this is reversed in the high-latitude ocean. The GPCP–CMAP spatial correlation is generally higher over land than over the ocean. The correlation between the global mean oceanic GPCP and CMAP is significantly low. It is very likely because the input data of the two products have much less in common over the ocean; in particular, the use of atoll data by the CMAP is disputable. The decreasing trend in the CMAP oceanic precipitation is found to be an artifact of input data change and atoll sampling error. In general, overocean precipitation represented by the GPCP is more reasonable; over land the two products are close, but different merging algorithms between the GPCP and the CMAP can sometimes produce substantial discrepancy in sensitive areas such as equatorial West Africa. EOF analysis shows that the GPCP and the CMAP are similar in 6 out of the first 10 modes, and the first 2 leading modes (ENSO patterns) of the GPCP are nearly identical to their counterparts of the CMAP. Input data changes [e.g., January 1986 for Geostationary Operational Environmental Satellite (GOES) precipitation index (GPI), July 1987 for Special Sensor Microwave Imager (SSM/I), May 1994 for Microwave Sounding Unit (MSU), and January 1996 for atolls] have implications in the behavior of the two datasets. Several abrupt changes identified in the statistics of the two datasets including the changes in overocean precipitation, spatial correlation time series, and some of the EOF principal components, can be related to one or more input data changes.

The Effect of Hydrometeors on MSU/AMSU Temperature Observations over the Tropical Ocean

2018 ◽  
Vol 35 (5) ◽  
pp. 1141-1150 ◽  
Author(s):  
Hamid A. Pahlavan ◽  
Qiang Fu ◽  
John M. Wallace
Keyword(s):  
Regression Coefficient ◽  
Temperature Fields ◽  
Tropospheric Temperature ◽  
Tropical Ocean ◽  
Microwave Sounding Unit ◽  
Mean Fields ◽  
Microwave Sounding ◽  
Temperature Observations ◽  
The Difference ◽  
Grid Points

AbstractThe temperature of Earth’s atmosphere has been monitored continuously since late 1978 by the Microwave Sounding Unit (MSU) and the Advanced Microwave Sounding Unit (AMSU) flown on polar-orbiting weather satellites. It is well known that these measurements are affected by the scattering and emission from hydrometeors, including cloud water, precipitation, and ice particles. In this study the hydrometeor effects on MSU/AMSU temperature observations are investigated by comparing satellite-observed temperature of the middle troposphere (TMT) with synthetic TMT constructed using temperature fields from ECMWF Interim [ERA-Interim (ERA-I)]. Precipitation data have been used to estimate how much of the difference between these two TMT fields is due to hydrometeor contamination effects. It is shown that there exists a robust linear proportionality between TMT deficit (i.e., the measured TMT minus the synthetic TMT) and precipitation at individual grid points in monthly mean fields. The linear correlation is even stronger in the annual mean and seasonally varying climatology and also in the spatial pattern of ENSO-related anomalies. The linear regression coefficient obtained in all of these analyses is virtually identical: −0.042 K (mm day−1)−1. The channel that senses lower-tropospheric temperature (TLT) is more sensitive to precipitation than the TMT channel: the regression coefficient is −0.059 K (mm day−1)−1. It is shown that correcting the TMT or TLT monthly anomalies by removing the hydrometeor contamination does not significantly influence estimates of tropical mean temperature trends, but it could affect the pattern of temperature trend over the tropical oceans.

scholarly journals Adequacy of Using a 1/3-Degree Special Sensor Microwave Imager Dataset to Estimate Climate-Scale Rainfall

2000 ◽  
Vol 39 (5) ◽  
pp. 680-685 ◽  
Author(s):  
Qihang Li ◽  
Ralph Ferraro ◽  
Norman Grody
Keyword(s):  
Data Processing ◽  
Spatial Resolution ◽  
Information Service ◽  
Global Precipitation Climatology Project ◽  
Spatial Density ◽  
Tropical Ocean ◽  
Full Resolution ◽  
Toga Coare ◽  
Microwave Imager ◽  
Climate Scale

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.

scholarly journals Improved Global Rainfall Retrieval Using the Special Sensor Microwave Imager (SSM/I)

2010 ◽  
Vol 49 (5) ◽  
pp. 1032-1043 ◽  
Author(s):  
Daniel Vila ◽  
Ralph Ferraro ◽  
Hilawe Semunegus
Keyword(s):  
Rainfall Variability ◽  
Substantial Reduction ◽  
Global Precipitation Climatology Project ◽  
Global Scale ◽  
Monthly Rainfall ◽  
Daily Data ◽  
Interannual Rainfall Variability ◽  
Microwave Imager ◽  
Global Precipitation ◽  
Rainfall Estimates

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.

scholarly journals An Updated TRMM Composite Climatology of Tropical Rainfall and Its Validation

2014 ◽  
Vol 27 (1) ◽  
pp. 273-284 ◽  
Author(s):  
Jian-Jian Wang ◽  
Robert F. Adler ◽  
George J. Huffman ◽  
David Bolvin
Keyword(s):  
Pacific Ocean ◽  
Tropical Rainfall Measuring Mission ◽  
Global Precipitation Climatology Project ◽  
Mean Value ◽  
Passive Microwave ◽  
Control Measures ◽  
Tropical Ocean ◽  
Tropical Rainfall ◽  
Uncertainty Estimates ◽  
Microwave Imager

Abstract An updated 15-yr Tropical Rainfall Measuring Mission (TRMM) composite climatology (TCC) is presented and evaluated. This climatology is based on a combination of individual rainfall estimates made with data from the primary TRMM instruments: the TRMM Microwave Imager (TMI) and the precipitation radar (PR). This combination climatology of passive microwave retrievals, radar-based retrievals, and an algorithm using both instruments simultaneously provides a consensus TRMM-based estimate of mean precipitation. The dispersion of the three estimates, as indicated by the standard deviation σ among the estimates, is presented as a measure of confidence in the final estimate and as an estimate of the uncertainty thereof. The procedures utilized by the compositing technique, including adjustments and quality-control measures, are described. The results give a mean value of the TCC of 4.3 mm day−1 for the deep tropical ocean belt between 10°N and 10°S, with lower values outside that band. In general, the TCC values confirm ocean estimates from the Global Precipitation Climatology Project (GPCP) analysis, which is based on passive microwave results adjusted for sampling by infrared-based estimates. The pattern of uncertainty estimates shown by σ is seen to be useful to indicate variations in confidence. Examples include differences between the eastern and western portions of the Pacific Ocean and high values in coastal and mountainous areas. Comparison of the TCC values (and the input products) to gauge analyses over land indicates the value of the radar-based estimates (small biases) and the limitations of the passive microwave algorithm (relatively large biases). Comparison with surface gauge information from western Pacific Ocean atolls shows a negative bias (~16%) for all the TRMM products, although the representativeness of the atoll gauges of open-ocean rainfall is still in question.

scholarly journals Special Sensor Microwave Imager (SSM/I) Intersensor Calibration Using a Simultaneous Conical Overpass Technique

2011 ◽  
Vol 50 (1) ◽  
pp. 77-95 ◽  
Author(s):  
Song Yang ◽  
Fuzhong Weng ◽  
Banghua Yan ◽  
Ninghai Sun ◽  
Mitch Goldberg
Keyword(s):  
Precipitable Water ◽  
Calibration Procedure ◽  
Environmental Data ◽  
Sensor Data ◽  
Global Ocean ◽  
Monthly Precipitation ◽  
Tropical Ocean ◽  
Climate Trend ◽  
Surface Precipitation ◽  
Microwave Imager

Abstract A new intersensor calibration scheme is developed for the Defense Meteorological Satellite Program Special Sensor Microwave Imager (SSM/I) to correct its scan-angle-dependent bias, the radar calibration beacon interference on the F-15 satellite, and other intersensor biases. The intersensor bias is characterized by the simultaneous overpass measurements with the F-13 SSM/I as a reference. This sensor data record (SDR) intersensor calibration procedure is routinely running at the National Oceanic and Atmospheric Administration and is now used for reprocessing all SSM/I environmental data records (EDR), including total precipitable water (TPW) and surface precipitation. Results show that this scheme improves the consistency of the monthly SDR’s time series from different SSM/I sensors. Relative to the matched rain products from the Tropical Rainfall Measuring Mission, the bias of SSM/I monthly precipitation is reduced by 12% after intersensor calibration. TPW biases between sensors are reduced by 75% over the global ocean and 20% over the tropical ocean, respectively. The intersensor calibration reduces biases by 20.6%, 15.7%, and 6.5% for oceanic, land, and global precipitation, respectively. The TPW climate trend is 1.59% decade−1 (or 0.34 mm decade−1) for the global ocean and 1.39% decade−1 (or 0.63 mm decade−1) for the tropical ocean, indicating related trends decrease of 38% and 54%, respectively, from the uncalibrated SDRs. Results demonstrate the large impacts of this calibration on the TPW climate trend.

scholarly journals Neural network for seasonal climate precipitation prediction on the Brazil

2020 ◽  
Vol 42 ◽  
pp. e15
Author(s):  
Juliana Aparecida Anochi ◽  
Haroldo Fraga de Campos Velho
Keyword(s):  
Neural Network ◽  
Optimization Problem ◽  
Global Precipitation Climatology Project ◽  
Reanalysis Data ◽  
Particle Collision ◽  
Monthly Precipitation ◽  
Multi Layer Perceptron ◽  
Meteorological Field ◽  
Precipitation Prediction ◽  
Global Precipitation

Precipitation is the hardest meteorological field to be predicted. An approach based on and optimal neural network is applied for climate precipitation prediction for the Brazil. A self-configurated multi-layer perceptron neural network (MLP-NN) is used as a predictor tool. The MLP-NN topology is found by solving an optimization problem by the Multi-Particle Collision Algorithm (MPCA). Prediction for Summer and Winter seasons are shown. The neural forecasting is evaluated by using the reanalysis data from the NCEP/NCAR and data from satellite GPCP (Global Precipitation Climatology Project -- monthly precipitation dataset).

scholarly journals Prototyping a Generic, Unified Land Surface Classification and Screening Methodology for GPM-Era Microwave Land Precipitation Retrieval Algorithms

2011 ◽  
Vol 50 (6) ◽  
pp. 1200-1211 ◽  
Author(s):  
Arief Sudradjat ◽  
Nai-Yu Wang ◽  
Kaushik Gopalan ◽  
Ralph R. Ferraro
Keyword(s):  
Land Surface ◽  
Tropical Rainfall Measuring Mission ◽  
Global Precipitation Climatology Project ◽  
Ancillary Data ◽  
Surface Cover ◽  
Cover Type ◽  
Retrieval Algorithms ◽  
Surface Classification ◽  
Microwave Imager ◽  
Global Precipitation

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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