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 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 Validation of CHIRPS Precipitation Estimates over Taiwan at Multiple Timescales

2021 ◽  
Vol 13 (2) ◽  
pp. 254 ◽  
Author(s):  
Jie Hsu ◽  
Wan-Ru Huang ◽  
Pin-Yi Liu ◽  
Xiuzhen Li
Keyword(s):  
Annual Cycle ◽  
Interannual Variation ◽  
Winter Precipitation ◽  
Monthly Precipitation ◽  
Precipitation Estimation ◽  
Precipitation Estimates ◽  
Precipitation Variation ◽  
Global Precipitation ◽  
Multiple Timescale ◽  
Better Than

The Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS), which incorporates satellite imagery and in situ station information, is a new high-resolution long-term precipitation dataset available since 1981. This study aims to understand the performance of the latest version of CHIRPS in depicting the multiple timescale precipitation variation over Taiwan. The analysis is focused on examining whether CHIRPS is better than another satellite precipitation product—the Integrated Multi-satellitE Retrievals for Global Precipitation Mission (GPM) final run (hereafter IMERG)—which is known to effectively capture the precipitation variation over Taiwan. We carried out the evaluations made for annual cycle, seasonal cycle, interannual variation, and daily variation during 2001–2019. Our results show that IMERG is slightly better than CHIRPS considering most of the features examined; however, CHIRPS performs better than that of IMERG in representing the (1) magnitude of the annual cycle of monthly precipitation climatology, (2) spatial distribution of the seasonal mean precipitation for all four seasons, (3) quantitative precipitation estimation of the interannual variation of area-averaged winter precipitation in Taiwan, and (4) occurrence frequency of the non-rainy grids in winter. Notably, despite the fact that CHIRPS is not better than IMERG for many examined features, CHIRPS can depict the temporal variation in precipitation over Taiwan on annual, seasonal, and interannual timescales with 95% significance. This highlights the potential use of CHIRPS in studying the multiple timescale variation in precipitation over Taiwan during the years 1981–2000, for which there are no data available in the IMERG database.

scholarly journals Using GRACE to Estitmate Snowfall Accumulation and Assess Gauge Undercatch Corrections in High Latitudes

2018 ◽  
Vol 31 (21) ◽  
pp. 8689-8704 ◽  
Author(s):  
Ali Behrangi ◽  
Alex Gardner ◽  
John T. Reager ◽  
Joshua B. Fisher ◽  
Daqing Yang ◽  
...  
Keyword(s):  
High Latitude ◽  
Surface Air Temperature ◽  
Global Precipitation Climatology Project ◽  
Dynamic Correction ◽  
Northern Asia ◽  
Near Surface ◽  
Precipitation Climatology ◽  
Global Precipitation Climatology Centre ◽  
Terrestrial Water ◽  
Global Precipitation

Ten years of terrestrial water storage anomalies from the Gravity Recovery and Climate Experiment (GRACE) were used to estimate high-latitude snowfall accumulation using a mass balance approach. The estimates were used to assess two common gauge-undercatch correction factors (CFs): the Legates climatology (CF-L) utilized in the Global Precipitation Climatology Project (GPCP) and the Fuchs dynamic correction model (CF-F) used in the Global Precipitation Climatology Centre (GPCC) monitoring product. The two CFs can be different by more than 50%. CF-L tended to exceed CF-F over northern Asia and Eurasia, while the opposite was observed over North America. Estimates of snowfall from GPCP, GPCC-L (GPCC corrected by CF-L), and GPCC-F (GPCC corrected by CF-F) were 62%, 64%, and 46% more than GPCC over northern Asia and Eurasia. The GRACE-based estimate (49% more than GPCC) was the closest to GPCC-F. We found that as near-surface air temperature decreased, the products increasingly underestimated the GRACE-based snowfall accumulation. Overall, GRACE showed that CFs are effective in improving GPCC estimates. Furthermore, our case studies and overall statistics suggest that CF-F is likely more effective than CF-L in most of the high-latitude regions studied here. GPCP showed generally better skill than GPCC-L, which might be related to the use of satellite data or additional quality controls on gauge inputs to GPCP. This study suggests that GPCP can be improved if it employs CF-L instead of CF-F to correct for gauge undercatch. However, this implementation requires further studies, region-specific analysis, and operational considerations.

scholarly journals Comparison of the GPCP 1DD Precipitation Product and NEXRAD Q2 Precipitation Estimates over the Continental United States

2016 ◽  
Vol 17 (6) ◽  
pp. 1837-1853 ◽  
Author(s):  
Wenjun Cui ◽  
Xiquan Dong ◽  
Baike Xi ◽  
Ronald Stenz
Keyword(s):  
United States ◽  
Warm Season ◽  
Correlation Coefficients ◽  
Heavy Rain ◽  
Global Precipitation Climatology Project ◽  
Cold Season ◽  
Precipitation Estimation ◽  
Precipitation Estimates ◽  
Annual Means ◽  
Mean Differences

Abstract This study compares the Global Precipitation Climatology Project (GPCP) 1 Degree Daily (1DD) precipitation estimates over the continental United States (CONUS) with National Mosaic and Multi-Sensor Quantitative Precipitation Estimation (NMQ) Next Generation (Q2) estimates. Spatial averages of monthly and yearly accumulated precipitation were computed based on daily estimates from six selected regions during the period 2010–12. Both Q2 and GPCP daily precipitation estimates show that precipitation amounts over southern regions (<40°N) are generally larger than northern regions (≥40°N). Correlation coefficients for daily estimates over selected regions range from 0.355 to 0.516 with mean differences (GPCP − Q2) varying from −0.86 to 0.99 mm. Better agreements are found in monthly estimates with the correlations varying from 0.635 to 0.787. For spatially averaged precipitation values averaged from grid boxes within selected regions, GPCP and Q2 estimates are well correlated, especially for monthly accumulated precipitation, with strong correlations ranging from 0.903 to 0.954. The comparisons between two datasets are also conducted for warm (April–September) and cold (October–March) seasons. During the warm season, GPCP estimates are 9.7% less than Q2 estimates, while during the cold season GPCP estimates exceed Q2 estimates by 6.9%. For precipitation over the CONUS, although annual means are close (978.54 for Q2 vs 941.79 mm for GPCP), Q2 estimates are much larger than GPCP over the central and southern United States and less than GPCP estimates in the northeastern United States. These results suggest that Q2 may have difficulties accurately estimating heavy rain and snow events, while GPCP may have an inability to capture some intense precipitation events, which warrants further investigation.

scholarly journals The verification of seasonal precipitation forecasts for early warning in Zambia and Malawi

2015 ◽  
Vol 12 (1) ◽  
pp. 31-36 ◽  
Author(s):  
O. Hyvärinen ◽  
L. Mtilatila ◽  
K. Pilli-Sihvola ◽  
A. Venäläinen ◽  
H. Gregow
Keyword(s):  
Regional Climate ◽  
Global Precipitation Climatology Project ◽  
Brier Score ◽  
Seasonal Precipitation ◽  
Seasonal Forecasts ◽  
African Countries ◽  
Precipitation Climatology ◽  
Global Precipitation Climatology Centre ◽  
Global Precipitation

Abstract. We assess the probabilistic seasonal precipitation forecasts issued by Regional Climate Outlook Forum (RCOF) for the area of two southern African countries, Malawi and Zambia from 2002 to 2013. The forecasts, issued in August, are of rainy season rainfall accumulations in three categories (above normal, normal, and below normal), for early season (October–December) and late season (January–March). As observations we used in-situ observations and interpolated precipitation products from Global Precipitation Climatology Project (GPCP), Global Precipitation Climatology Centre (GPCC), and Climate Prediction Centre (CPC) Merged Analysis of Precipitation (CMAP). Differences between results from different data products are smaller than confidence intervals calculated by bootstrap. We focus on below normal forecasts as they were deemed to be the most important for society. The well-known decomposition of Brier score into three terms (Reliability, Resolution, and Uncertainty) shows that the forecasts are rather reliable or well-calibrated, but have a very low resolution; that is, they are not able to discriminate different events. The forecasts also lack sharpness as forecasts for one category are rarely higher than 40 % or less than 25 %. However, these results might be unnecessarily pessimistic, because seasonal forecasts have gone through much development during the period when the forecasts verified in this paper were issued, and forecasts using current methodology might have performed better.

Snow depth trends from CMIP6 models conflict with observational evidence

2021 ◽  
pp. 1-40
Keyword(s):  
Northern Hemisphere ◽  
Air Temperature ◽  
High Latitude ◽  
Snow Depth ◽  
Cold Season ◽  
Observational Evidence ◽  
High Quality ◽  
Observational Dataset ◽  
Precipitation Increase

Abstract In this study, we compiled a high-quality, in situ observational dataset to evaluate snow depth simulations from 22 CMIP6 models across high-latitude regions of the Northern Hemisphere over the period 1955–2014. Simulated snow depths have low accuracy (RMSE = 17–36 cm) and are biased high, exceeding the observed baseline (1976–2005) on average (18 ± 16 cm) across the study area. Spatial climatological patterns based on observations are modestly reproduced by the models (NRMSDs of 0.77 ± 0.20). Observed snow depth during the cold season increased by about 2.0 cm over the study period, which is approximately 11% relative to the baseline. The models reproduce decreasing snow depth trends that contradict the observations, but they all indicate a precipitation increase during the cold season. The modeled snow depths are insensitive to precipitation but too sensitive to air temperature; these inaccurate sensitivities could explain the discrepancies between the observed and simulated snow depth trends. Based on our findings, we recommend caution when using and interpreting simulated changes in snow depth and associated impacts.

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.

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.

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