Evaluation of Satellite-Retrieved Extreme Precipitation over Europe using Gauge Observations

2014 ◽  
Vol 27 (2) ◽  
pp. 607-623 ◽  
Author(s):  
M. Lockhoff ◽  
O. Zolina ◽  
C. Simmer ◽  
J. Schulz
Keyword(s):  
Variable Parameter ◽  
Global Precipitation Climatology Project ◽  
Precipitation Variability ◽  
Spatial And Temporal Variability ◽  
Evaluation Approach ◽  
Fuzzy Methods ◽  
Verification Methods ◽  
Precipitation Estimates ◽  
Rain Rates ◽  
Global Precipitation

Abstract Climate change is expected to change precipitation characteristics and particularly the frequency and magnitude of precipitation extremes. Satellite observations form an important part of the observing system necessary to monitor both temporal and spatial patterns of precipitation variability and extremes. As satellite-based precipitation estimates are generally only indirect, however, their reliability has to be verified. This study evaluates the ability of the satellite-based Global Precipitation Climatology Project One-Degree Daily (GPCP1DD) dataset to reliably reproduce precipitation variability and extremes over Europe compared to the European Daily High-resolution Observational Gridded Dataset (E-OBS). The results show that the two datasets agree reasonably well not only when looking at climatological statistics such as climatological mean, number of wet days (rain rates 1 mm), and mean intensity (i.e., mean over all wet days) but also with respect to their distributions. The results also reveal a pronounced seasonal cycle in the performance of GPCP1DD that is worse in winter and spring. Both deterministic and fuzzy verification methods are used to assess the ability of the GPCP1DD dataset to capture extremes. Fuzzy methods prove to be the better suited evaluation approach for such a highly variable parameter as precipitation because it compensates for slight spatial and temporal displacements. Whereas the deterministic diagnostics confirm previous findings on the deficiencies of satellite products, the “fuzzy” results show that at larger spatiotemporal scales (e.g., 3°/5 days) GPCP1DD has useful skill and is able to reliably represent the spatial and temporal variability of extremes.

scholarly journals Comparison of GPCP Monthly and Daily Precipitation Estimates with High-Latitude Gauge Observations

2009 ◽  
Vol 48 (9) ◽  
pp. 1843-1857 ◽  
Author(s):  
David T. Bolvin ◽  
Robert F. Adler ◽  
George J. Huffman ◽  
Eric J. Nelkin ◽  
Jani P. Poutiainen
Keyword(s):  
High Latitude ◽  
Global Precipitation Climatology Project ◽  
Precipitation Event ◽  
Finnish Meteorological Institute ◽  
Monthly Scale ◽  
Precipitation Measurement ◽  
Precipitation Estimates ◽  
Wide Variability ◽  
Global Precipitation Measurement ◽  
Global Precipitation

Abstract Monthly and daily products of the Global Precipitation Climatology Project (GPCP) are evaluated through a comparison with Finnish Meteorological Institute (FMI) gauge observations for the period January 1995–December 2007 to assess the quality of the GPCP estimates at high latitudes. At the monthly scale both the final GPCP combination satellite–gauge (SG) product is evaluated, along with the satellite-only multisatellite (MS) product. The GPCP daily product is scaled to sum to the monthly product, so it implicitly contains monthly-scale gauge influence, although it contains no daily gauge information. As expected, the monthly SG product agrees well with the FMI observations because of the inclusion of limited gauge information. Over the entire analysis period the SG estimates are biased low by 6% when the same wind-loss adjustment is applied to the FMI gauges as is used in the SG analysis. The interannual anomaly correlation is about 0.9. The satellite-only MS product has a lesser, but still reasonably good, interannual correlation (∼0.6) while retaining a similar bias due to the use of a climatological bias adjustment. These results indicate the value of using even a few gauges in the analysis and provide an estimate of the correlation error to be expected in the SG analysis over ocean and remote land areas where gauges are absent. The daily GPCP precipitation estimates compare reasonably well at the 1° latitude × 2° longitude scale with the FMI gauge observations in the summer with a correlation of 0.55, but less so in the winter with a correlation of 0.45. Correlations increase somewhat when larger areas and multiday periods are analyzed. The day-to-day occurrence of precipitation is captured fairly well by the GPCP estimates, but the corresponding precipitation event amounts tend to show wide variability. The results of this study indicate that the GPCP monthly and daily fields are useful for meteorological and hydrological studies but that there is significant room for improvement of satellite retrievals and analysis techniques in this region. It is hoped that the research here provides a framework for future high-latitude assessment efforts such as those that will be necessary for the upcoming satellite-based Global Precipitation Measurement (GPM) mission.

scholarly journals Reprocessed, Bias-Corrected CMORPH Global High-Resolution Precipitation Estimates from 1998

2017 ◽  
Vol 18 (6) ◽  
pp. 1617-1641 ◽  
Author(s):  
Pingping Xie ◽  
Robert Joyce ◽  
Shaorong Wu ◽  
Soo-Hyun Yoo ◽  
Yelena Yarosh ◽  
...  
Keyword(s):  
High Resolution ◽  
Global Precipitation Climatology Project ◽  
Temporal Variations ◽  
Cold Season ◽  
Superior Performance ◽  
Climate Data ◽  
Integration Algorithm ◽  
Satellite Precipitation ◽  
Precipitation Estimates ◽  
Global Precipitation

Abstract The Climate Prediction Center (CPC) morphing technique (CMORPH) satellite precipitation estimates are reprocessed and bias corrected on an 8 km × 8 km grid over the globe (60°S–60°N) and in a 30-min temporal resolution for an 18-yr period from January 1998 to the present to form a climate data record (CDR) of high-resolution global precipitation analysis. First, the purely satellite-based CMORPH precipitation estimates (raw CMORPH) are reprocessed. The integration algorithm is fixed and the input level 2 passive microwave (PMW) retrievals of instantaneous precipitation rates are from identical versions throughout the entire data period. Bias correction is then performed for the raw CMORPH through probability density function (PDF) matching against the CPC daily gauge analysis over land and through adjustment against the Global Precipitation Climatology Project (GPCP) pentad merged analysis of precipitation over ocean. The reprocessed, bias-corrected CMORPH exhibits improved performance in representing the magnitude, spatial distribution patterns, and temporal variations of precipitation over the global domain from 60°S to 60°N. Bias in the CMORPH satellite precipitation estimates is almost completely removed over land during warm seasons (May–September), while during cold seasons (October–April) CMORPH tends to underestimate the precipitation due to the less-than-desirable performance of the current-generation PMW retrievals in detecting and quantifying snowfall and cold season rainfall. An intercomparison study indicated that the reprocessed, bias-corrected CMORPH exhibits consistently superior performance than the widely used TRMM 3B42 (TMPA) in representing both daily and 3-hourly precipitation over the contiguous United States and other global regions.

scholarly journals An Update on the Oceanic Precipitation Rate and Its Zonal Distribution in Light of Advanced Observations from Space

2014 ◽  
Vol 27 (11) ◽  
pp. 3957-3965 ◽  
Author(s):  
Ali Behrangi ◽  
Graeme Stephens ◽  
Robert F. Adler ◽  
George J. Huffman ◽  
Bjorn Lambrigtsen ◽  
...  
Keyword(s):  
Tropical Rainfall Measuring Mission ◽  
High Sensitivity ◽  
Global Precipitation Climatology Project ◽  
Precipitation Rate ◽  
Global Ocean ◽  
Zonal Distribution ◽  
Earth Observing System ◽  
Precipitation Estimates ◽  
Global Precipitation ◽  
Oceanic Precipitation

Abstract This study contributes to the estimation of the global mean and zonal distribution of oceanic precipitation rate using complementary information from advanced precipitation measuring sensors and provides an independent reference to assess current precipitation products. Precipitation estimates from the Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR) and CloudSat cloud profiling radar (CPR) were merged, as the two complementary sensors yield an unprecedented range of sensitivity to quantify rainfall from drizzle through the most intense rates. At higher latitudes, where TRMM PR does not exist, precipitation estimates from Aqua’s Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) complemented CloudSat CPR to capture intense precipitation rates. The high sensitivity of CPR allows estimation of snow rate, an important type of precipitation at high latitudes, not directly observed in current merged precipitation products. Using the merged precipitation estimate from the CloudSat, TRMM, and Aqua platforms (this estimate is abbreviated to MCTA), the authors’ estimate for 3-yr (2007–09) near-global (80°S–80°N) oceanic mean precipitation rate is ~2.94 mm day−1. This new estimate of mean global ocean precipitation is about 9% higher than that of the corresponding Climate Prediction Center (CPC) Merged Analysis of Precipitation (CMAP) value (2.68 mm day−1) and about 4% higher than that of the Global Precipitation Climatology Project (GPCP; 2.82 mm day−1). Furthermore, MCTA suggests distinct differences in the zonal distribution of precipitation rate from that depicted in GPCP and CMAP, especially in the Southern Hemisphere.

scholarly journals Assessing High-Latitude Winter Precipitation from Global Precipitation Analyses Using GRACE

2010 ◽  
Vol 11 (2) ◽  
pp. 405-420 ◽  
Author(s):  
Sean Swenson
Keyword(s):  
High Latitude ◽  
Global Precipitation Climatology Project ◽  
Cold Season ◽  
Winter Precipitation ◽  
Precipitation Estimates ◽  
Highly Correlated ◽  
Gravity Recovery ◽  
Global Precipitation ◽  
Climate Prediction Center

Abstract This study compares cold-season, high-latitude precipitation estimates from two global, merged satellite–gauge precipitation analyses—Global Precipitation Climatology Project (GPCP) and Climate Prediction Center Merged Analysis of Precipitation (CMAP)—to total water storage anomalies produced from the Gravity Recovery and Climate Experiment (GRACE). In general, spatial patterns and interannual variability are highly correlated between the datasets, although significant differences are also observed. Differences vary by region but typically increase at higher latitudes. Furthermore, results indicate that the gauge undercatch correction used by GPCP may be overestimated. These comparisons may be useful for assessing precipitation estimates over large regions, where in situ gauge networks may be sparse.

scholarly journals A Detailed Evaluation of GPCP 1° Daily Rainfall Estimates over the Mississippi River Basin

2005 ◽  
Vol 44 (5) ◽  
pp. 665-681 ◽  
Author(s):  
Mekonnen Gebremichael ◽  
Witold F. Krajewski ◽  
Mark L. Morrissey ◽  
George J. Huffman ◽  
Robert F. Adler
Keyword(s):  
River Basin ◽  
Mississippi River ◽  
Sampling Error ◽  
Evaluation Criteria ◽  
Daily Rainfall ◽  
Global Precipitation Climatology Project ◽  
Spatial And Temporal Variability ◽  
Mississippi River Basin ◽  
Error Statistics ◽  
Precipitation Estimates

Abstract This study provides an intensive evaluation of the Global Precipitation Climatology Project (GPCP) 1° daily (1DD) rainfall products over the Mississippi River basin, which covers 435 1° latitude × 1° longitude grids for the period of January 1997–December 2000 using radar-based precipitation estimates. The authors’ evaluation criteria include unconditional continuous, conditional (quasi) continuous, and categorical statistics, and their analyses cover annual and seasonal time periods. The authors present spatial maps that reflect the results for the 1° grids and a summary of the results for three selected regions. They also develop a statistical framework that partitions the GPCP–radar difference statistics into GPCP error and radar error statistics. They further partition the GPCP error statistics into sampling error and retrieval error statistics and estimate the sampling error statistics using a data-based resampling experiment. Highlights of the results include the following: 1) the GPCP 1DD product captures the spatial and temporal variability of rainfall to a high degree, with more than 80% of the variance explained, 2) the GPCP 1DD product proficiently detects rainy days at a large range of rainfall thresholds, and 3) in comparison with radar-based estimates the GPCP 1DD product overestimates rainfall.

scholarly journals Toward the Use of Integral Radar Volume Descriptors for Quantitative Areal Precipitation Estimation: Results from Pseudoradar Observations

2009 ◽  
Vol 26 (9) ◽  
pp. 1798-1813 ◽  
Author(s):  
Silke Trömel ◽  
Clemens Simmer ◽  
Jürgen Braun ◽  
Thomas Gerstner ◽  
Michael Griebel
Keyword(s):  
Weather Forecasting ◽  
Spatial And Temporal Variability ◽  
Volume Data ◽  
Orographic Rainfall ◽  
Precipitation Estimation ◽  
Precipitation Estimates ◽  
Rain Events ◽  
Relative Errors ◽  
Rain Rates

Abstract The central objective of this analysis is to significantly enhance the quality of radar-derived precipitation estimates by as fully as possible exploiting the information contained in the spatial and temporal variability of 3D radar volume data. The results presented are based on pseudoradar data and rain rates of a regional weather forecasting model and 12 true radiosoundings as well. Two approaches are pursued: the first approach estimates total rainfall from an individual storm over its lifetime, whereas the second approach assesses the areawide instantaneous rainfall from a multiplicity of such storms by the use of measurements of the areal coverage of the storms exceeding a threshold radar reflectivity. The concept is extended by adding more predictors to significantly enhance the rainfall estimates. The horizontal expected value and the horizontal standard deviation of enclosed reflectivities at the ground, the mean brightband fraction and its trend, the fractional area with reflectivities exceeding a threshold τ, and an orographic rainfall amplifier provide relative errors smaller than 10% in approximately 75% of the considered rain events in the first approach. In the second approach, a relative error is achieved that is below 10% in approximately 63% elements of the test set.

scholarly journals PERSIANN-CCS-CDR, a 3-hourly 0.04° global precipitation climate data record for heavy precipitation studies

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Mojtaba Sadeghi ◽  
Phu Nguyen ◽  
Matin Rahnamay Naeini ◽  
Kuolin Hsu ◽  
Dan Braithwaite ◽  
...  
Keyword(s):  
Spatial Resolution ◽  
Heavy Precipitation ◽  
Climate Data ◽  
Spatiotemporal Resolution ◽  
Data Record ◽  
Precipitation Estimation ◽  
Precipitation Estimates ◽  
Global Precipitation ◽  
Climate Data Record

AbstractAccurate long-term global precipitation estimates, especially for heavy precipitation rates, at fine spatial and temporal resolutions is vital for a wide variety of climatological studies. Most of the available operational precipitation estimation datasets provide either high spatial resolution with short-term duration estimates or lower spatial resolution with long-term duration estimates. Furthermore, previous research has stressed that most of the available satellite-based precipitation products show poor performance for capturing extreme events at high temporal resolution. Therefore, there is a need for a precipitation product that reliably detects heavy precipitation rates with fine spatiotemporal resolution and a longer period of record. Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification System-Climate Data Record (PERSIANN-CCS-CDR) is designed to address these limitations. This dataset provides precipitation estimates at 0.04° spatial and 3-hourly temporal resolutions from 1983 to present over the global domain of 60°S to 60°N. Evaluations of PERSIANN-CCS-CDR and PERSIANN-CDR against gauge and radar observations show the better performance of PERSIANN-CCS-CDR in representing the spatiotemporal resolution, magnitude, and spatial distribution patterns of precipitation, especially for extreme events.

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