Global Precipitation Climatology Project Data Research Articles

Evaluation of CMIP6 Models Skill in Representing Annual Extreme Precipitation over Northern and Southern Nigeria

In this study, we evaluate the performance of 13 climate models from phase 6 of the Coupled Model Intercomparison Project (CMIP6) in simulating seven (7) extreme precipitation indices over Northern and southern Nigeria. The considered extreme indices designated in this study were, Total precipitation (PRCPTOT), maximum consecutive wet days (CWD), Heavy precipitation days (R10mm), Very heavy precipitation days (R20mm), Max-5 days Precipitation (RX5day), Extremely wet days (R99pTOT) and Very wet days (R95pTOT). The performance of these 13 climate models are assesed by comparing the model simulation to the observed dataset from the Global Precipitation Climatology Project (GPCP). The performance of CMIP6 models in capturing extreme precipitation characteristics is revealed through some selected multiple descriptive statistics: the normalized mean bias error, RMSE, NRMSE, and Taylor diagram. The descriptive statistics conclusively revealed the satisfactory performance of Cmip6 models in simulation of most extreme events over the north and southern region of Nigeria, as the selected 13 climate models showed a high statistical correlation of (~0.8) when compared with the observed GPCP data except for maximum consecutive wet days (CWD). Overall, majority of CMIP6 models were able to accurately represented only six (6) of the extreme indices, and a significant majority of CMIP6 models failed in simulating maximum consecutive wet days (CWD) in both northern and southern Nigeria.

4DVD visualization and delivery of the 20th century reanalysis data: methods and examples

This paper describes the use of a web application system, called 4-Dimensional Visual Delivery (4DVD), to visualize and deliver climate data. The focus of this paper is on the monthly data from the NOAA-CIRES-DOE Twentieth Century Reanalysis (20CR) project Version 2c. The 20CR is a global model at a spatial resolution about 2° latitude-longitude. The temporal coverage is from January 1851 to December 2014. The monthly data include numerous climate variables, such as temperature, pressure, precipitation rate, zonal wind, meridional wind, albedo, and longwave radiation flux. We show how to use the 4DVD system to conveniently visualize, deliver, and analyze the 20CR data of these parameters on a globe or on a 2D latitude-longitude map. We analyze the temporal and spatial patterns of zonal wind and precipitation over Nino 4 region (5° N-5° S, 160° E-150° W). The monthly NASA Global Precipitation Climatology Project (GPCP) data are used to compare the results from 20CR. GPCP data used in this 4DVD application are defined on 2.5° latitude-longitude grid boxes from January 1979 to January 2019. The 4DVD figures successfully show the reverse of zonal wind direction from the east-to-west wind to the west-to-east wind on the grid box (2° S, 176° W) in the Nino 4 region during an El Nino event. The spectral analysis shows the annual and semi-annual cycles in the zonal wind data on this grid box. The GPCP data are used to support the analysis results of 20CR zonal data. The precipitation data over the grid box (1.25° S, 176.25° W) in the Nino 4 region show clear El Nino signals (persistent extreme precipitation more than 6 mm/day) and clear La Nina signals (persistent “dry” with near-zero precipitation). The spectral analysis of the precipitation data time series over this grid box shows no annual cycle.

State-of-the-art climate modeling of extreme precipitation over Africa: analysis of CORDEX added-value over CMIP5

The ability of the state-of-the-art Global and Regional Climate Models (GCMs and RCMs) to reproduce the mean and spatial characteristics of extreme precipitation indices over Africa is evaluated. In particular, the extent to which CORDEX (COordinated Regional Downscaling Experiment) adds useful details on the performance of CMIP5 (Coupled Model Intercomparison Project Phase 5) multimodel ensemble is investigated. Comparison of the present day simulation was performed with two precipitation observation datasets, the high-resolution TRMM (Tropical Rainfall Measuring Mission) and coarse resolution GPCP (Global Precipitation Climatology Project), to evaluate models strengths and weaknesses. Eight indices generated from absolute (1 mm) and percentile (95th) based thresholds as defined by the Expert Team on Climate Change Detection and Indices (ETCCDI) are computed for seventeen CMIP5 GCMs and six CORDEX RCMs (for twelve downscaling experiments) for each year during the historical period (1975–2004). Our results suggest a good consistency between GPCP and TRMM in producing annual mean and frequency of extreme precipitation events over Africa despite few inconsistencies. However, their associated intensities largely differed from one another with GPCP data displaying drier bias. Overall, multimodel ensembles simulations overestimate the frequency of extreme precipitation events and underestimate their intensities. The results further show that CMIP5 exhibits wet bias of precipitation events and drier bias of precipitation intensity than CORDEX, and that CORDEX produces precipitation magnitude within the range of the observations and more in line with the higher-resolution TRMM data. This illustrates the added value achieved with the higher-resolution CORDEX multimodel ensemble for the simulation of such events, and points toward the use of these RCMs to study extreme precipitation for a better assessment of climate change over Africa.

Intercomparison of precipitation datasets for summer precipitation characteristics over East Asia

Precipitation data in the Global Precipitation Climatology Project (GPCP) and in four reanalysis datasets, ERA-Interim, MERRA, NCEP/NCAR, and JRA, are compared against the CPC Merged Precipitation (CMAP) in the cyclostationary empirical orthogonal function (CSEOF) space to evaluate these datasets in representing the summer precipitation characteristics over East Asia. CSEOF analysis is applied to each dataset, and regression analysis is performed in the CSEOF space with the CMAP data as the target. The regression analysis establishes one-to-one correspondence between the CSEOF loading vectors of the target variable and those of the predictors, i.e., GPCP and the four reanalysis datasets. The loading vectors of the GPCP data coincide almost exactly with those of the CMAP data, i.e., the two observation-based precipitation datasets represent practically identical summer precipitation characteristics over East Asia. The reanalysis datasets also reproduce the first five CSEOF modes reasonably; however, performance of NCEP/NCAR is notably lower than others. The re-constructed precipitation using the first five regressed CSEOF modes of the reanalysis datasets are well correlated with that of the CMAP data with reasonably large correlation coefficients, suggesting that these reanalysis precipitation products reliably simulate the major summer precipitation characteristics in East Asia. All of the four reanalysis products commonly show noticeable errors in representing the summer rainfall over the mid-latitude ocean to the south of Japan, the tropical western Pacific, tropical/subtropical regions including the Indochina Peninsula, India, the Maritime Continent, and regions of complex terrain especially those characterized by strong orographic slopes around the Tibetan Plateau. The errors over the regions of complex orography and coastal lines may be partially due to the inability of reanalysis models in simulating the effects of complex terrain and the lack of observations in these sparsely populated regions.

Comparison of GCM Precipitation Predictions with Their RMSEs and Pattern Correlation Coefficients

This study evaluated 20 general circulation models (GCMs) of the Coupled Model Intercomparison Project, Phase 5 (CMIP5), which provide the prediction results for the period of 2006 to 2014, the period from which the observation data (the Global Precipitation Climatology Project (GPCP) data) are available. Both the GCM predictions of precipitation and the GPCP data were compared for three data structures—the global, zonal, and grid mean—with conventional statistics like the root mean square error (RMSE) and the pattern correlation coefficient of the cyclostationary empirical orthogonal functions (CSEOFs). As a result, it was possible to select a GCM which showed the best performance among the 20 GCMs considered in this study. Overall, the NorSM1-M model was found to be the most similar to the GPCP data. Additionally, the IPSL-CM5A-LR, BCC-CSM, and GFDL-CMS models were also found to be quite similar to the GPCP data.

Estimation of river based transportable volcanic material distribution using satellite DEM and precipitation data

The transportable volcanic materials of Mt. Marapi, which are deposited around the caldera, as a result from last eruption in November 2015, must be estimated as a first step to handle the lahar/ debris flow disaster. In this research, the method used to determine the amount of river-based transportable volcanic material distribution was offered. The LIDAR Satellite DEM and TRMM data from Global Precipitation Climatology Project (GPCP) have been used to estimate the deposited volcanic materials. Based on the GPCP data analysis, it was found that the rainfall pattern distribute into two area, which are 0.52 mm/ hr on west side and 0.61 mm/ hr on east side relative to the mountain summit. The deposited materials from Mt. Marapi 14 November 2015 eruption (volcanic boulders and lava) were located in the upstream of six prioritized watershed. The transportable volcanic material will predominantly flows to the South and North West direction. The potentially transported boulder and lava are around 80.33 % of the total erupted material that are deposited in the river upstream. Batang Kadurang Watershed has the highest transportable material of 1,905.18 m3. The results of study can be used as a rapid disaster countermeasure for lahar disaster mitigation.

Spatial and temporal variability of precipitation over the Mediterranean Basin based on 32-year satellite Global Precipitation Climatology Project data. Part-II: inter-annual variability and trends

Monthly mean satellite data from the Global Precipitation Climatology Project (GPCPv2) are used to examine the year-by-year variability of precipitation over the Mediterranean Basin and its changes over the period 1979–2010. The results show that the mean annual precipitation averaged over the study area has slightly increased from 1979 to 2010 by 1.28 mm or by 0.2% (trend not statistically significant at the 95% confidence level). Nevertheless, examining the trends at a local scale, spatial and temporal patterns are revealed, with opposite trends in adjacent areas and increasing precipitation in summer and autumn against almost unchanged or decreasing precipitation in winter and spring, respectively. Inter-decadal changes of precipitation are detected, with precipitation decreasing in the 1980s, then increasing through the late 1990s and finally declining in the 2000s before levelling off since 2007. These changes are significantly anti-correlated (R = −0.57, up to −0.66 in winter) with the North Atlantic Oscillation (NAO) index, thus confirming the critical role of this large-scale teleconnection for the regional precipitation over the basin.

Spatial and temporal variability of precipitation over the Mediterranean Basin based on 32‐year satellite Global Precipitation Climatology Project data, part I: evaluation and climatological patterns

ABSTRACTThe precipitation regime over the Mediterranean basin is investigated for the period 1979–2010 using monthly mean satellite data from the Global Precipitation Climatology Project (GPCPv2). The results show that a clear contrast exists between the more rainy northern part of the study region (Southern Europe) and the drier southern area (North Africa, Iberian Peninsula) and between the western sides (rainsides) of the Iberian, Italian and Balkan peninsulas and their eastern sides (rainshadows). The mean annual precipitation averaged over the study area is P = 593 ± 203 mm year−1, but it has a strong spatial variability ranging from 20 mm year−1 (North Africa) to 1500 mm year−1 (Alps). A significant seasonal variability exists, with the early winter and late autumn months (November and December) being the wettest with precipitation amounts larger than 60 mm month−1. The GPCPv2 satellite precipitation data are satisfactorily correlated with rain gauge measurements from 433 stations within the study area (correlation coefficient R = 0.78 for all stations on a yearly basis, with values ranging between 0.72 and 0.82, depending on the season) with a slight overestimation. They also compare well with the higher spatial and temporal resolution Tropical Rainfall Measuring Mission (TRMM) data, which supports the validity of the present study.

Trends of regional precipitation and their control mechanisms during 1979–2013

Trends in precipitation are critical to water resources. Considerable uncertainty remains concerning the trends of regional precipitation in response to global warming and their controlling mechanisms. Here, we use an interannual difference method to derive trends of regional precipitation from GPCP (Global Precipitation Climatology Project) data and MERRA (Modern- Era Retrospective Analysis for Research and Applications) reanalysis in the near-global domain of 60°S–60°N during a major global warming period of 1979–2013. We find that trends of regional annual precipitation are primarily driven by changes in the top 30% heavy precipitation events, which in turn are controlled by changes in precipitable water in response to global warming, i.e., by thermodynamic processes. Significant drying trends are found in most parts of the U.S. and eastern Canada, the Middle East, and eastern South America, while significant increases in precipitation occur in northern Australia, southern Africa, western India and western China. In addition, as the climate warms there are extensive enhancements and expansions of the three major tropical precipitation centers–the Maritime Continent, Central America, and tropical Africa–leading to the observed widening of Hadley cells and a significant strengthening of the global hydrological cycle.

Decadal trends of the annual amplitude of global precipitation

AbstractIn this study, decadal trends of the annual amplitude of global precipitation are compared in Climate Prediction Center (CPC) Merged Analysis of Precipitation (CMAP), Global Precipitation Climatology Project (GPCP), and National Centers for Environmental Prediction (NCEP) reanalysis data sets. The analysis reveals decreasing trends in the CMAP and reanalysis data and a flat trend in the GPCP data. The decreasing trends are mainly associated with the increasing trend of low annual minimum precipitation rate in the CMAP data and high annual minimum precipitation rate in the reanalysis data. The trend in the GPCP data is flat because of the balance between decreasing trends along equatorial oceans and increasing trends over subtropical oceans.

Trends of extreme precipitation in eastern China and their possible causes

Significant increases of heavy precipitation and decreases of light precipitation have been reported over widespread regions of the globe. Global warming and effects of anthropogenic aerosols have both been proposed as possible causes of these changes. We examine data from urban and rural meteorological stations in eastern China (1955–2011) and compare them with Global Precipitation Climatology Project (GPCP) data (1979–2007) and reanalysis data in various latitude zones to study changes in precipitation extremes. Significant decreases in light precipitation and increases in heavy precipitation are found at both rural and urban stations, as well as low latitudes over the ocean, while total precipitation shows little change. Characteristics of these changes and changes in the equatorial zone and other latitudes suggest that global warming rather than aerosol effects is the primary cause of the changes. In eastern China, increases of annual total dry days (28 days) and ≥10 consecutive dry days (36%) are due to the decrease in light precipitation days, thereby establishing a causal link among global warming, changes in precipitation extremes, and higher meteorological risk of floods and droughts. Further, results derived from the GPCP data and reanalysis data suggest that the causal link exists over widespread regions of the globe.

An algorithm for integrating satellite precipitation estimates with in situ precipitation data on a pentad time scale

AbstractThis study proposes an algorithm for constructing pentad precipitation fields by integrating the popularly used Global Precipitation Climatology Project (GPCP) daily precipitation data set, GPCP1dd v1.2, with Canadian in situ daily precipitation data. This algorithm consists of two major steps. First, the GPCP data were adjusted to remove biases relative to the gauge data, with consideration of the differences between snowfall and rainfall, and of the gauge density. Then, a blended pentad precipitation field was constructed using the adjusted GPCP precipitation field and the differences between the gauge and adjusted GPCP precipitation fields (residual kriging). The skill of the algorithm is evaluated for three networks of sparse to medium gauge density, with the evaluation data set being much larger than the training data set. The results show that the algorithm produces better representation of pentad precipitation fields than the GPCP precipitation estimates or using the gauge data alone; it has smaller root‐mean‐square errors and higher correlation skill scores. This algorithm was used to produce the first blended pentad precipitation data set for the period of 1997–2007 for Canada (CanBP5dV1). It can be used for other regions around the world.

Precipitation climatology over India: validation with observations and reanalysis datasets and spatial trends

Changing rainfall patterns have significant effect on water resources, agriculture output in many countries, especially the country like India where the economy depends on rain-fed agriculture. Rainfall over India has large spatial as well as temporal variability. To understand the variability in rainfall, spatial–temporal analyses of rainfall have been studied by using 107 (1901–2007) years of daily gridded India Meteorological Department (IMD) rainfall datasets. Further, the validation of IMD precipitation data is carried out with different observational and different reanalysis datasets during the period from 1989 to 2007. The Global Precipitation Climatology Project data shows similar features as that of IMD with high degree of comparison, whereas Asian Precipitation-Highly-Resolved Observational Data Integration Towards Evaluation data show similar features but with large differences, especially over northwest, west coast and western Himalayas. Spatially, large deviation is observed in the interior peninsula during the monsoon season with National Aeronautics Space Administration-Modern Era Retrospective-analysis for Research and Applications (NASA-MERRA), pre-monsoon with Japanese 25 years Re Analysis (JRA-25), and post-monsoon with climate forecast system reanalysis (CFSR) reanalysis datasets. Among the reanalysis datasets, European Centre for Medium-Range Weather Forecasts Interim Re-Analysis (ERA-Interim) shows good comparison followed by CFSR, NASA-MERRA, and JRA-25. Further, for the first time, with high resolution and long-term IMD data, the spatial distribution of trends is estimated using robust regression analysis technique on the annual and seasonal rainfall data with respect to different regions of India. Significant positive and negative trends are noticed in the whole time series of data during the monsoon season. The northeast and west coast of the Indian region shows significant positive trends and negative trends over western Himalayas and north central Indian region.

Testing gridded land precipitation data and precipitation and runoff reanalyses (1982–2010) between 45° S and 45° N with normalised difference vegetation index data

Abstract. The realistic simulation of key components of the land-surface hydrological cycle – precipitation, runoff, evaporation and transpiration, in general circulation models of the atmosphere – is crucial to assess adverse weather impacts on environment and society. Here, gridded precipitation data from observations and precipitation and runoff fields from reanalyses were tested with satellite derived global vegetation index data for 1982–2010 and latitudes between 45° S and 45° N. Data were obtained from the Climate Research Unit (CRU), the Global Precipitation Climatology Project (GPCP) and Tropical Rainfall Monitoring Mission (TRMM; analysed for 1998–2010 only) and precipitation and runoff reanalyses were obtained from the National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR), the European Centre for Medium-Range Weather Forecasts (ECMWF) and the NASA Global Modelling and Assimilation Office (GMAO). Annual land-surface precipitation was converted to annual potential vegetation net primary productivity (NPP) and was compared to mean annual normalised difference vegetation index (NDVI) data measured by the Advanced Very High Resolution Radiometer (AVHRR; 1982–1999) and Moderate Resolution Imaging Spectroradiometer (MODIS; 2001–2010). The effect of spatial resolution on the agreement between NPP and NDVI was investigated as well. The CRU and TRMM derived NPP agreed most closely with the NDVI data. The GPCP data showed weaker spatial agreement, largely because of their lower spatial resolution, but similar temporal agreement. MERRA Land and ERA Interim precipitation reanalyses showed similar spatial agreement to the GPCP data and good temporal agreement in semi-arid regions of the Americas, Asia, Australia and southern Africa. The NCEP/NCAR reanalysis showed the lowest spatial agreement, which could only in part be explained by its lower spatial resolution. No reanalysis showed realistic interannual precipitation variations for northern tropical Africa. Inclusion of runoff in the NPP prediction resulted only in marginally better agreement for the MERRA Land reanalysis and slightly worse agreement for the NCEP/NCAR and ERA Interim reanalyses.

Decadal trends of global precipitation in the recent 30 years

AbstractDecadal trends of global precipitation are examined using the Climate Prediction Center (CPC) Merged Analysis of Precipitation (CMAP), Global Precipitation Climatology Project (GPCP), and National Centers for Environmental Prediction (NCEP)/National Center for Atmospheric Research (NCAR) reanalysis data. The decadal trends of global precipitation average diverge a decreasing trend for the CMAP data, a flat trend for the GPCP data, and an increasing trend for the reanalysis data. The decreasing trend for the CMAP data is associated with the reduction in high precipitation. The flat trend for the GPCP data is related to the offset between the increase in high precipitation and the decrease in low precipitation. The increasing trend for the reanalysis data corresponds to the increase in high precipitation.

The impact of tropical western Pacific convection on the North Pacific atmospheric circulation during the boreal winter

In this study, we investigate the impact of atmospheric convection over the western tropical Pacific (100–145°E, 0–20°N) on the boreal winter North Pacific atmosphere flow by analyzing National Center for Environmental Prediction Reanalysis 1, Extended Reconstructed Sea Surface Temperature and Global Precipitation Climatology Project data. The western tropical Pacific convection is not only the main energy source driving the local Hadley and Walker circulations, but it also significantly influences North Pacific circulation, by modifying a mid-latitude Jet stream through the connection with the local Hadley circulation. On the one hand, this strong convection leads to a northward expansion of local Hadley cells simultaneous with a northward movement of the western North Pacific jet because of the close correlation between the Jet and Hadley circulation boundaries. On the other hand, this strong convection also intensifies tropical Pacific Walker circulation, which reduces the eastern Pacific sea surface temperature, resembling a La Nina state through the enhanced equatorial upwelling. The cooling of the eastern tropical Pacific has an inter-tropical convergence zone located further north; thus, the local Hadley circulation moves northward. As a result, the jet axis over the eastern North Pacific, which also corresponds to the boundary of the local Hadley circulation, moves to higher latitude. Consequently, this northward movement of the Jet axis over the North Pacific is reflected as a northwest–southeast dipole sea level pressure (SLP) pattern. The composite analysis of SLP over the North Pacific against the omega (Ω) (Pa/s) at 500 hPa over the western tropical Pacific actually reveals that this northwest-southeast dipole structure is attributed to the intensified tropical western Pacific convection, which pushes the Pacific Jet to the north. Finally we also analyzed south Pacific for the austral winter as did previously to North Pacific, and found that the results were consistent.

Monthly Predicted Flow Values of the Sanaga River in Cameroon Using Neural Networks Applied to GLDAS, MERRA and GPCP Data

The aim of our study is to predict the discharge rate of the river Sanaga using neural network techniques. Our investigations have taken place in the Sanaga watershed area in Cameroon. The measurement station is situated in the locality of Edea-Song-Mbengue (04°04’15”N, 10°27’50”E) where we have obtained monthly values of the river Sanaga discharge rates that have been measured in situ from January 1989 to December 2004. We have trained neural networks (NN), each with data of parameters such as the surface albedo, the total cloud fraction, the evaporation, the outgoing longwave radiation, the air temperature, the specific humidity, the surface runoff and the precipitation height. The precipitation values have been obtained from GPCP (Global Precipitation Climatology Project) and those of the other parameters from the data assimilation systems GLDAS (Global Land Data Assimilation System) and MERRA (Modern Era-Retrospective analysis for Research and Application). As desired outputs of the NN during the learning process, we have used the measured river runoff values. After introducing temporal delays of 01 and 02 months in the learning-process, we could observe the presence of the memory effect of the parameters used on the temporal evolution of the river discharge rate. After analysis of the performance's criteria of the NN with the help of the calculated Root Means Square Errors (RMSE) and determination coefficients between predicted values and in situ observed ones, we have perceived that the NN which takes into account the two-month delay can predict the river discharge rate with a strong correlation.

A near real-time satellite-based global drought climate data record

Reliable drought monitoring requires long-term and continuous precipitation data. High resolution satellite measurements provide valuable precipitation information on a quasi-global scale. However, their short lengths of records limit their applications in drought monitoring. In addition to this limitation, long-term low resolution satellite-based gauge-adjusted data sets such as the Global Precipitation Climatology Project (GPCP) one are not available in near real-time form for timely drought monitoring. This study bridges the gap between low resolution long-term satellite gauge-adjusted data and the emerging high resolution satellite precipitation data sets to create a long-term climate data record of droughts. To accomplish this, a Bayesian correction algorithm is used to combine GPCP data with real-time satellite precipitation data sets for drought monitoring and analysis. The results showed that the combined data sets after the Bayesian correction were a significant improvement compared to the uncorrected data. Furthermore, several recent major droughts such as the 2011 Texas, 2010 Amazon and 2010 Horn of Africa droughts were detected in the combined real-time and long-term satellite observations. This highlights the potential application of satellite precipitation data for regional to global drought monitoring. The final product is a real-time data-driven satellite-based standardized precipitation index that can be used for drought monitoring especially over remote and/or ungauged regions.

Quality Control and Analysis of Global Gauge-Based Daily Precipitation Dataset from 1980 to 2009

A series of quality control (QC) procedures were performed on a gauge-based global daily precipitation dataset from the Global Telecommunication System (GTS) for the period 1980–2009. A new global daily precipitation (NGDP) dataset was constructed by applying those QC procedures to eliminate erroneous records. The NGDP dataset was evaluated using the NOAA Climate Prediction Center Merged Analysis of Precipitation (CMAP) and the Global Precipitation Climatology Project (GPCP) precipitation datasets. The results showed that the frequency distribution and spatial distribution pattern of NGDP had a nice match with those from the CMAP and GPCP datasets. The global mean correlation coefficients with the CMAP and GPCP data increased from 0.24 for original GTS precipitation data to about 0.70 for NGDP data. Correspondingly, the root mean square errors (RMSE) decreased from 12 mm per day to 1 mm per day. The interannual variabilities of NGDP monthly precipitation are consistent with the CMAP and GPCP datasets in Asia. Meanwhile, the seasonal variabilities for most land areas on the Earth of NGDP dataset are also consistent with the CMAP and GPCP precipitation products. CitationNie, S.-P., Y. Luo, W.-P. Li, et al., 2012: Quality control and analysis of global gauge-based daily precipitation dataset from 1980 to 2009. Adv. Clim. Change Res., 3(1), doi: 10.3724/SP.J.1248.2012.00045.

Evaporation Correction Methods for Microwave Retrievals of Surface Precipitation Rate

Active and passive microwave remote sensing estimates of surface precipitation based on signals from hydrometeors aloft require correction for evaporated precipitation that would otherwise reach the ground. This paper develops and compares two near-surface evaporation correction methods using two years of data from 509 globally distributed rain gauges and three passive millimeter-wave Advanced Microwave Sounding Units (AMSUs) aboard National Oceanic and Atmospheric Administration (NOAA) satellites (NOAA-15, NOAA-16, and NOAA-18). The first type of correction is a scale factor that minimizes the bias between the means of annual AMSU and rain gauge precipitation accumulations (in millimeters per year) for each of 12 infrared-based surface classifications. The scale factor for the second correction method is computed using a neural network trained using both surface classification and annual average relative humidity pro files. AMSU surface precipitation retrievals using both methods were compared to the annual accumulations for 509 rain gauges uncorrected for wind effects, where different data were used for training and accuracy evaluation. The rms annual accumulation retrieval errors for AMSU using surface classification and relative humidity corrections were 236 and 222 mm/y, respectively, compared to 190 mm/y for corresponding Global Precipitation Climatology Project data, which utilizes more satellite sensors and over 6500 rain gauges.