scholarly journals Quantitative Evaluations and Error Source Analysis of Fengyun-2-Based and GPM-Based Precipitation Products over Mainland China in Summer, 2018

2019 ◽  
Vol 11 (24) ◽  
pp. 2992 ◽  
Author(s):  
Jintao Xu ◽  
Ziqiang Ma ◽  
Guoqiang Tang ◽  
Qingwen Ji ◽  
Xiaoxiao Min ◽  
...  
Keyword(s):  
Water Cycle ◽  
Mainland China ◽  
Correlation Coefficients ◽  
Rain Gauge ◽  
Source Analysis ◽  
Rain Gauge Data ◽  
Gauge Data ◽  
Precipitation Estimates ◽  
Quantitative Precipitation Estimates ◽  
Global Precipitation

Satellite-based quantitative precipitation estimates (QPE) with a fine quality are of great importance to global water cycle and matter and energy exchange research. In this study, we firstly apply various statistical indicators to evaluate and compare the main current satellite-based precipitation products from Chinese Fengyun (FY)-2 and the Global Precipitation Measurement (GPM), respectively, over mainland China in summer, 2018. We find that (1) FY-2G QPE and Integrated Multi-satellitE Retrievals for GPM (IMERG) perform significantly better than FY-2E QPE, using rain gauge data, with correlation coefficients (CC) varying from 0.65 to 0.90, 0.80 to 0.90, and 0.40 to 0.53, respectively; (2) IMERG agrees well with rain gauge data at monthly scale, while it performs worse than FY-2G QPE at hourly and daily scales, which may be caused by its algorithms; (3) FY-2G QPE underestimates the precipitation in summer, while FY-2E QPE and IMERG generally overestimate the precipitation; (4) there is an interesting error phenomenon in that both FY-based and GPM-based precipitation products perform more poorly during the period from 06:00 to 10:00 UTC than other periods at diurnal scale; and (5) FY-2G QPE agrees well with IMERG in terms of spatial patterns and consistency (CC of ~0.81). These findings can provide valuable preliminary references for improving next generation satellite-based QPE retrieval algorithms and instructions for applying these data in various practical fields.

scholarly journals Improving Multisensor Precipitation Estimation via Adaptive Conditional Bias–Penalized Merging of Rain Gauge Data and Remotely Sensed Quantitative Precipitation Estimates

2019 ◽  
Vol 20 (12) ◽  
pp. 2347-2365 ◽  
Author(s):  
Ali Jozaghi ◽  
Mohammad Nabatian ◽  
Seongjin Noh ◽  
Dong-Jun Seo ◽  
Lin Tang ◽  
...  
Keyword(s):  
Computational Cost ◽  
Mean Field ◽  
Remotely Sensed ◽  
Rain Gauge ◽  
Rain Gauge Data ◽  
Gauge Data ◽  
Precipitation Estimation ◽  
Precipitation Estimates ◽  
Radar Satellite ◽  
Quantitative Precipitation Estimates

Abstract We describe and evaluate adaptive conditional bias–penalized cokriging (CBPCK) for improved multisensor precipitation estimation using rain gauge data and remotely sensed quantitative precipitation estimates (QPE). The remotely sensed QPEs used are radar-only and radar–satellite-fused estimates. For comparative evaluation, true validation is carried out over the continental United States (CONUS) for 13–30 September 2015 and 7–9 October 2016. The hourly gauge data, radar-only QPE, and satellite QPE used are from the Hydrometeorological Automated Data System, Multi-Radar Multi-Sensor System, and Self-Calibrating Multivariate Precipitation Retrieval (SCaMPR), respectively. For radar–satellite fusion, conditional bias–penalized Fisher estimation is used. The reference merging technique compared is ordinary cokriging (OCK) used in the National Weather Service Multisensor Precipitation Estimator. It is shown that, beyond the reduction due to mean field bias (MFB) correction, both OCK and adaptive CBPCK additionally reduce the unconditional root-mean-square error (RMSE) of radar-only QPE by 9%–16% over the CONUS for the two periods, and that adaptive CBPCK is superior to OCK for estimation of hourly amounts exceeding 1 mm. When fused with the MFB-corrected radar QPE, the MFB-corrected SCaMPR QPE for September 2015 reduces the unconditional RMSE of the MFB-corrected radar by 4% and 6% over the entire and western half of the CONUS, respectively, but is inferior to the MFB-corrected radar for estimation of hourly amounts exceeding 7 mm. Adaptive CBPCK should hence be favored over OCK for estimation of significant amounts of precipitation despite larger computational cost, and the SCaMPR QPE should be used selectively in multisensor QPE.

scholarly journals Radar-Based Precipitation Climatology in Germany—Developments, Uncertainties and Potentials

Atmosphere ◽  
2020 ◽  
Vol 11 (2) ◽  
pp. 217 ◽  
Author(s):  
Jennifer Kreklow ◽  
Björn Tetzlaff ◽  
Benjamin Burkhard ◽  
Gerald Kuhnt
Keyword(s):  
Ground Truth ◽  
Winter Precipitation ◽  
Rain Gauge ◽  
Rain Gauge Data ◽  
Weather Radars ◽  
Hourly Rainfall ◽  
Gauge Data ◽  
Precipitation Estimates ◽  
Quantitative Precipitation Estimates

Precipitation is a crucial driver for many environmental processes and weather radars are capable of providing precipitation information with high spatial and temporal resolution. However, radar-based quantitative precipitation estimates (QPE) are also subject to various potential uncertainties. This study explored the development, uncertainties and potentials of the hourly operational German radar-based and gauge-adjusted QPE called RADOLAN and its reanalyzed radar climatology dataset named RADKLIM in comparison to ground-truth rain gauge data. The precipitation datasets were statistically analyzed across various time scales ranging from annual and seasonal aggregations to hourly rainfall intensities in regard to their capability to map long-term precipitation distribution, to detect low intensity rainfall and to capture heavy rainfall. Moreover, the impacts of season, orography and distance from the radar on long-term precipitation sums were examined in order to evaluate dataset performance and to describe inherent biases. Results revealed that both radar products tend to underestimate total precipitation sums and particularly high intensity rainfall. However, our analyses also showed significant improvements throughout the RADOLAN time series as well as major advances through the climatologic reanalysis regarding the correction of typical radar artefacts, orographic and winter precipitation as well as range-dependent attenuation.

scholarly journals Radar-based Precipitation Climatology in Germany – Developments, Uncertainties and Potentials

2020 ◽  
Author(s):  
Jennifer Kreklow ◽  
Björn Tetzlaff ◽  
Benjamin Burkhard ◽  
Gerald Kuhnt
Keyword(s):  
Ground Truth ◽  
Winter Precipitation ◽  
Rain Gauge ◽  
Rain Gauge Data ◽  
Weather Radars ◽  
Hourly Rainfall ◽  
Gauge Data ◽  
Precipitation Estimates ◽  
Quantitative Precipitation Estimates

Precipitation is a crucial driver for many environmental processes and weather radars are capable of providing precipitation information with high spatial and temporal resolution. However, radar-based quantitative precipitation estimates (QPE) are also subject to various potential uncertainties. This study explores the development, uncertainties and potentials of the hourly operational German radar-based and gauge-adjusted QPE called RADOLAN and its reanalysed radar climatology dataset named RADKLIM in comparison to ground-truth rain gauge data. The precipitation datasets are statistically analysed across various time scales ranging from annual and seasonal aggregations to hourly rainfall intensities in regard to their capability to map long-term precipitation distribution, to detect low intensity rainfall and to capture heavy rainfall. Moreover, the impacts of season, orography and distance from the radar on long-term precipitation sums are examined in order to evaluate dataset performance and to describe inherent biases. Results revealed that both radar products tend to underestimate total precipitation sums and particularly high intensity rainfall. But our analyses also showed significant improvements throughout the RADOLAN time series as well as major advances through the climatologic reanalysis regarding the correction of typical radar artefacts, orographic and winter precipitation as well as range-dependent attenuation.

NEXRAD NWS Polarimetric Precipitation Product Evaluation for IFloodS

2015 ◽  
Vol 16 (4) ◽  
pp. 1676-1699 ◽  
Author(s):  
Luciana K. Cunha ◽  
James A. Smith ◽  
Witold F. Krajewski ◽  
Mary Lynn Baeck ◽  
Bong-Chul Seo
Keyword(s):  
Product Evaluation ◽  
Rain Gauge ◽  
Current Version ◽  
Rain Gauge Data ◽  
Gauge Data ◽  
Precipitation Estimates ◽  
Diagnostic Framework ◽  
Single Polarization ◽  
Quantitative Precipitation Estimates ◽  
Rain Gauge Network

Abstract The NEXRAD program has recently upgraded the WSR-88D network observational capability with dual polarization (DP). In this study, DP quantitative precipitation estimates (QPEs) provided by the current version of the NWS system are evaluated using a dense rain gauge network and two other single-polarization (SP) rainfall products. The analyses are performed for the period and spatial domain of the Iowa Flood Studies (IFloodS) campaign. It is demonstrated that the current version (2014) of QPE from DP is not superior to that from SP mainly because DP QPE equations introduce larger bias than the conventional rainfall–reflectivity [i.e., R(Z)] relationship for some hydrometeor types. Moreover, since the QPE algorithm is based on hydrometeor type, abrupt transitions in the phase of hydrometeors introduce errors in QPE with surprising variation in space that cannot be easily corrected using rain gauge data. In addition, the propagation of QPE uncertainties across multiple hydrological scales is investigated using a diagnostic framework. The proposed method allows us to quantify QPE uncertainties at hydrologically relevant scales and provides information for the evaluation of hydrological studies forced by these rainfall datasets.

scholarly journals Evaluation of Satellite Precipitation Estimates over Australia

2020 ◽  
Vol 12 (4) ◽  
pp. 678 ◽  
Author(s):  
Zhi-Weng Chua ◽  
Yuriy Kuleshov ◽  
Andrew Watkins
Keyword(s):  
Rainfall Intensity ◽  
Austral Summer ◽  
Rain Gauge ◽  
Reference Dataset ◽  
Rain Gauge Data ◽  
Satellite Precipitation ◽  
Gauge Data ◽  
Precipitation Estimates ◽  
Notable Increase ◽  
Climate Prediction Center

This study evaluates the U.S. National Oceanographic and Atmospheric Administration’s (NOAA) Climate Prediction Center morphing technique (CMORPH) and the Japan Aerospace Exploration Agency’s (JAXA) Global Satellite Mapping of Precipitation (GSMaP) satellite precipitation estimates over Australia across an 18 year period from 2001 to 2018. The evaluation was performed on a monthly time scale and used both point and gridded rain gauge data as the reference dataset. Overall statistics demonstrated that satellite precipitation estimates did exhibit skill over Australia and that gauge-blending yielded a notable increase in performance. Dependencies of performance on geography, season, and rainfall intensity were also investigated. The skill of satellite precipitation detection was reduced in areas of elevated topography and where cold frontal rainfall was the main precipitation source. Areas where rain gauge coverage was sparse also exhibited reduced skill. In terms of seasons, the performance was relatively similar across the year, with austral summer (DJF) exhibiting slightly better performance. The skill of the satellite precipitation estimates was highly dependent on rainfall intensity. The highest skill was obtained for moderate rainfall amounts (2–4 mm/day). There was an overestimation of low-end rainfall amounts and an underestimation in both the frequency and amount for high-end rainfall. Overall, CMORPH and GSMaP datasets were evaluated as useful sources of satellite precipitation estimates over Australia.

scholarly journals WHU-SGCC: A novel approach for blending daily satellite (CHIRP) and precipitation observations over Jinsha River Basin

2019 ◽  
Author(s):  
Gaoyun Shen ◽  
Nengcheng Chen ◽  
Wei Wang ◽  
Zeqiang Chen
Keyword(s):  
River Basin ◽  
Daily Precipitation ◽  
Rain Gauge ◽  
Jinsha River ◽  
Rain Gauge Data ◽  
Rain Gauges ◽  
Novel Approach ◽  
Gauge Data ◽  
Precipitation Estimates ◽  
Hydrological Applications

Abstract. Accurate and consistent satellite-based precipitation estimates blended with rain gauge data are important for regional precipitation monitoring and hydrological applications, especially in regions with limited rain gauges. However, existing fusion precipitation estimates often have large uncertainties over mountainous areas with complex topography and sparse rain gauges, and the existing data blending algorithms are very bad at removing the day-by-day random errors. Therefore, the development of effective methods for high-accuracy precipitation estimates over complex terrain and on a daily scale is of vital importance for mountainous hydrological applications. This study aims to offer a novel approach for blending daily precipitation gauge data, gridded precipitation data and the Climate Hazards Group Infrared Precipitation (CHIRP, daily, 0.05°) satellite-derived precipitation estimates over the Jinsha River Basin for the period of June–July–August in 2016. This method is named the Wuhan University Satellite and Gauge precipitation Collaborated Correction (WHU-SGCC). The results show that the WHU-SGCC method is effective in precipitation bias adjustments from point to surface, which is evaluated by categorical indices. Moreover, the accuracy of the spatial distribution of the precipitation estimates derived from the WHU-SGCC method is related to the complexity of the topography. The validation also verifies that the proposed approach is effective in the detection of precipitation events that are less than 20 mm. This study indicates that the WHU-SGCC approach is a promising tool to monitor monsoon precipitation over Jinsha River Basin, the complicated mountainous terrain with sparse rain gauge data, considering the spatial correlation and the historical precipitation characteristics. The daily precipitation estimations at 0.05° resolution over Jinsha River Basin in summer 2016, derived from WHU-SGCC are available at the PANGAEA Data Publisher for Earth & Environmental Science portal (https://doi.pangaea.de/10.1594/PANGAEA.896615).

scholarly journals A Rainfall Data Intercomparison Dataset of RADKLIM, RADOLAN, and Rain Gauge Data for Germany

Data ◽  
2019 ◽  
Vol 4 (3) ◽  
pp. 118 ◽  
Author(s):  
Kreklow ◽  
Tetzlaff ◽  
Kuhnt ◽  
Burkhard
Keyword(s):  
Radar Data ◽  
Rain Gauge ◽  
Rainfall Data ◽  
Rain Gauge Data ◽  
Additional Information ◽  
Weather Radars ◽  
Gauge Data ◽  
Quality Checks ◽  
Quantitative Precipitation Estimates ◽  
Rainfall Statistics

Quantitative precipitation estimates (QPE) derived from weather radars provide spatially and temporally highly resolved rainfall data. However, they are also subject to systematic and random bias and various potential uncertainties and therefore require thorough quality checks before usage. The dataset described in this paper is a collection of precipitation statistics calculated from the hourly nationwide German RADKLIM and RADOLAN QPEs provided by the German Weather Service (Deutscher Wetterdienst (DWD)), which were combined with rainfall statistics derived from rain gauge data for intercomparison. Moreover, additional information on parameters that can potentially influence radar data quality, such as the height above sea level, information on wind energy plants and the distance to the next radar station, were included in the dataset. The resulting two point shapefiles are readable with all common GIS and constitutes a spatially highly resolved rainfall statistics geodataset for the period 2006 to 2017, which can be used for statistical rainfall analyses or for the derivation of model inputs. Furthermore, the publication of this data collection has the potential to benefit other users who intend to use precipitation data for any purpose in Germany and to identify the rainfall dataset that is best suited for their application by a straightforward comparison of three rainfall datasets without any tedious data processing and georeferencing.

scholarly journals Evaluation of TRMM rainfall estimates over a large Indian river basin (Mahanadi)

2014 ◽  
Vol 18 (7) ◽  
pp. 2493-2502 ◽  
Author(s):  
D. Kneis ◽  
C. Chatterjee ◽  
R. Singh
Keyword(s):  
Real Time ◽  
Stream Flow ◽  
Rain Gauge ◽  
Rainfall Runoff ◽  
Runoff Simulation ◽  
Rain Gauge Data ◽  
Gauge Data ◽  
Precipitation Estimates ◽  
Rainfall Estimates

Abstract. The paper examines the quality of satellite-based precipitation estimates for the lower Mahanadi River basin (eastern India). The considered data sets known as 3B42 and 3B42-RT (version 7/7A) are routinely produced by the tropical rainfall measuring mission (TRMM) from passive microwave and infrared recordings. While the 3B42-RT data are disseminated in real time, the gauge-adjusted 3B42 data set is published with a delay of some months. The quality of the two products was assessed in a two-step procedure. First, the correspondence between the remotely sensed precipitation rates and rain gauge data was evaluated at the sub-basin scale. Second, the quality of the rainfall estimates was assessed by analysing their performance in the context of rainfall–runoff simulation. At sub-basin level (4000 to 16 000 km2) the satellite-based areal precipitation estimates were found to be moderately correlated with the gauge-based counterparts (R2 of 0.64–0.74 for 3B42 and 0.59–0.72 for 3B42-RT). Significant discrepancies between TRMM data and ground observations were identified at high-intensity levels. The rainfall depth derived from rain gauge data is often not reflected by the TRMM estimates (hit rate < 0.6 for ground-based intensities > 80 mm day-1). At the same time, the remotely sensed rainfall rates frequently exceed the gauge-based equivalents (false alarm ratios of 0.2–0.6). In addition, the real-time product 3B42-RT was found to suffer from a spatially consistent negative bias. Since the regionalisation of rain gauge data is potentially associated with a number of errors, the above results are subject to uncertainty. Hence, a validation against independent information, such as stream flow, was essential. In this case study, the outcome of rainfall–runoff simulation experiments was consistent with the above-mentioned findings. The best fit between observed and simulated stream flow was obtained if rain gauge data were used as model input (Nash–Sutcliffe index of 0.76–0.88 at gauges not affected by reservoir operation). This compares to the values of 0.71–0.78 for the gauge-adjusted TRMM 3B42 data and 0.65–0.77 for the 3B42-RT real-time data. Whether the 3B42-RT data are useful in the context of operational runoff prediction in spite of the identified problems remains a question for further research.

scholarly journals Application of GSMaP Product and Rain Gauge Data for Monitoring Rainfall Condition of Flood Events in Indonesia

2017 ◽  
Vol 1 (1) ◽  
Author(s):  
Nyoman Sugiartha ◽  
Kakuji Ogawara ◽  
Tasuku Tanaka ◽  
Made Sudiana Mahendra
Keyword(s):  
Correlation Coefficients ◽  
Intensity Variation ◽  
Rain Gauge ◽  
Flood Events ◽  
Entire Study ◽  
Rain Gauge Data ◽  
Rainfall Condition ◽  
In Situ Data ◽  
Gauge Data ◽  
Daily Scale

Rainfall is one of main causes of floods. Monitoring of the rainfall is essential for understanding flood mechanism and early warning. Ground-based rain gauge is a conventional device to measure rainfall amount and considered as a point measurement. Satellite-based rainfall estimates provides complement measurement over wide area having few or even no in situ data. This study evaluates rainfall intensity variation and patterns preceding flood events in Indonesia for the period of 2003-2010 using the GSMaP_MVK satellite-based rainfall product with one hour and 0.1o x 0.1o resolutions and rain gauge station data as a benchmark. The analysed data are 3-hourly average and daily accumulation time steps. The chosen research locations were Medan City, Pekanbaru City, Indragiri Hulu Regency, Samarinda City and Manado City. Graphical comparisons of the GSMaP_MVK with the rain gauge data show discrepancies in capturing rainfall events and intensity. The GSMaP_MVK performs underestimation for the most areas, except Samarinda City, which is overestimated. Short-term period rainfall pattern is the most frequent occurred preceding flood events for the entire study areas which indicate that the areas are more susceptible to flash floods and river overflows. Overall, the GSMaP_MVK product provides promising potentiality for the application of monitoring rainfall conditions preceding flood events over the research locations. Statistical verifications reveal that on average, correlation coefficients are (0.22-0.54) and (0.65-0.83) for 3-hourly and daily scale, respectively. While, probability of rain detections (PODs) are (0.57-0.75) and (0.93-0.99), accordingly.

scholarly journals WHU-SGCC: a novel approach for blending daily satellite (CHIRP) and precipitation observations over the Jinsha River basin

2019 ◽  
Vol 11 (4) ◽  
pp. 1711-1744 ◽  
Author(s):  
Gaoyun Shen ◽  
Nengcheng Chen ◽  
Wei Wang ◽  
Zeqiang Chen
Keyword(s):  
River Basin ◽  
Daily Precipitation ◽  
Rain Gauge ◽  
Jinsha River ◽  
Rain Gauge Data ◽  
Rain Gauges ◽  
Novel Approach ◽  
Gauge Data ◽  
Precipitation Estimates ◽  
Hydrological Applications

Abstract. Accurate and consistent satellite-based precipitation estimates blended with rain gauge data are important for regional precipitation monitoring and hydrological applications, especially in regions with limited rain gauges. However, the existing fusion precipitation estimates often have large uncertainties over mountainous areas with complex topography and sparse rain gauges, and most of the existing data blending algorithms are not good at removing the day-by-day errors. Therefore, the development of effective methods for high-accuracy precipitation estimates over complex terrain and at a daily scale is of vital importance for mountainous hydrological applications. This study aims to offer a novel approach for blending daily precipitation gauge data and the Climate Hazards Group Infrared Precipitation (CHIRP; daily, 0.05∘) satellite-derived precipitation developed by UC Santa Barbara over the Jinsha River basin from 1994 to 2014. This method is called the Wuhan University Satellite and Gauge precipitation Collaborated Correction (WHU-SGCC). The results show that the WHU-SGCC method is effective for liquid precipitation bias adjustments from points to surfaces as evaluated by multiple error statistics and from different perspectives. Compared with CHIRP and CHIRP with station data (CHIRPS), the precipitation adjusted by the WHU-SGCC method has greater accuracy, with overall average improvements of the Pearson correlation coefficient (PCC) by 0.0082–0.2232 and 0.0612–0.3243, respectively, and decreases in the root mean square error (RMSE) by 0.0922–0.65 and 0.2249–2.9525 mm, respectively. In addition, the Nash–Sutcliffe efficiency coefficient (NSE) of the WHU-SGCC provides more substantial improvements than CHIRP and CHIRPS, which reached 0.2836, 0.2944, and 0.1853 in the spring, autumn, and winter. Daily accuracy evaluations indicate that the WHU-SGCC method has the best ability to reduce precipitation bias, with average reductions of 21.68 % and 31.44 % compared to CHIRP and CHIRPS, respectively. Moreover, the accuracy of the spatial distribution of the precipitation estimates derived from the WHU-SGCC method is related to the complexity of the topography. The validation also verifies that the proposed approach is effective at detecting major precipitation events within the Jinsha River basin. In spite of the correction, the uncertainties in the seasonal precipitation forecasts in the summer and winter are still large, which might be due to the homogenization attenuating the extreme rain event estimates. However, the WHU-SGCC approach may serve as a promising tool to monitor daily precipitation over the Jinsha River basin, which contains complicated mountainous terrain with sparse rain gauge data, based on the spatial correlation and the historical precipitation characteristics. The daily precipitation estimations at the 0.05∘ resolution over the Jinsha River basin during all four seasons from 1990 to 2014, derived from WHU-SGCC, are available at the PANGAEA Data Publisher for Earth &amp; Environmental Science portal (https://doi.org/10.1594/PANGAEA.905376, Shen et al., 2019).

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