scholarly journals Precipitation downscaling using a probability-matching approach and geostationary infrared data: an evaluation over six climate regions

2018 ◽  
Vol 22 (7) ◽  
pp. 3685-3699 ◽  
Author(s):  
Ruifang Guo ◽  
Yuanbo Liu ◽  
Han Zhou ◽  
Yaqiao Zhu
Keyword(s):  
Water Cycle ◽  
Regional Scale ◽  
Rain Gauge ◽  
Cumulative Distribution ◽  
Rainfall Regime ◽  
Precipitation Data ◽  
Main Hypothesis ◽  
Basin Scale ◽  
Spatio Temporal ◽  
Better Than

Abstract. Precipitation is one of the most important components of the global water cycle. Precipitation data at high spatial and temporal resolutions are crucial for basin-scale hydrological and meteorological studies. In this study, we propose a cumulative distribution of frequency (CDF)-based downscaling method (DCDF) to obtain hourly 0.05∘ × 0.05∘ precipitation data. The main hypothesis is that a variable with the same resolution of target data should produce a CDF that is similar to the reference data. The method was demonstrated using the 3-hourly 0.25∘ × 0.25∘ Climate Prediction Center morphing method (CMORPH) dataset and the hourly 0.05∘ × 0.05∘ FY2-E geostationary (GEO) infrared (IR) temperature brightness (Tb) data. Initially, power function relationships were established between the precipitation rate and Tb for each 1∘ × 1∘ region. Then the CMORPH data were downscaled to 0.05∘ × 0.05∘. The downscaled results were validated over diverse rainfall regimes in China. Within each rainfall regime, the fitting functions' coefficients were able to implicitly reflect the characteristics of precipitation. Quantitatively, the downscaled estimates not only improved spatio-temporal resolutions, but also performed better (bias: −7.35–10.35 %; correlation coefficient, CC: 0.48–0.60) than the CMORPH product (bias: 20.82–94.19 %; CC: 0.31–0.59) over convective precipitating regions. The downscaled results performed as well as the CMORPH product over regions dominated with frontal rain systems and performed relatively poorly over mountainous or hilly areas where orographic rain systems dominate. Qualitatively, at the daily scale, DCDF and CMORPH had nearly equivalent performances at the regional scale, and 79 % DCDF may perform better than or nearly equivalently to CMORPH at the point (rain gauge) scale. The downscaled estimates were able to capture more details about rainfall motion and changes under the condition that DCDF performs better than or nearly equivalently to CMORPH.

scholarly journals Precipitation downscaling using a probability-matching approach and geostationary infrared data: An evaluation over six climate regions

2017 ◽  
Author(s):  
Ruifang Guo ◽  
Yuanbo Liu ◽  
Han Zhou ◽  
Yaqiao Zhu
Keyword(s):  
Water Cycle ◽  
Rain Gauge ◽  
Cumulative Distribution ◽  
Rainfall Regime ◽  
Precipitation Data ◽  
Main Hypothesis ◽  
Global Water Cycle ◽  
Basin Scale ◽  
Daily Scale ◽  
Spatio Temporal

Abstract. Precipitation is one of the most important components of the global water cycle. Precipitation data at high spatial and temporal resolutions are crucial for basin-scale hydrological and meteorological studies. In this study, we proposed a cumulative distribution of frequency (CDF)-based downscaling method (DCDF) to obtain hourly 0.05° × 0.05° precipitation data. The main hypothesis is that a variable with the same resolution of target data should produce a CDF that is similar to the reference data. The method was demonstrated using the 3 hourly 0.25° × 0.25° Climate Prediction Center Morphing method (CMORPH) dataset and the hourly 0.05° × 0.05° FY2-E Geostationary (GEO) Infrared (IR) temperature brightness (Tb) data. Initially, power function relationships were established between precipitation rate and Tb for each 1° × 1° region. Then the CMORPH data were downscaled to 0.05° × 0.05°. The downscaled results were validated over diverse rainfall regimes in China. Within each rainfall regime, the fitting functions coefficients were able to implicitly reflect the characteristics of precipitation. Qualitatively, the downscaled estimates were able to capture more details about rainfall motions and changes. Quantitatively, the time series of the downscaled estimates were more similar to the rain gauge data than the original CMORPH product at the daily scale. The downscaled estimates not only improved spatio-temporal resolutions, but also performed better (Bias: −7.35 %~10.35 %; correlation coefficient (CC): 0.48~0.60) than the CMORPH product (Bias: 20.82 %~94.19 %; CC: 0.31~0.59) over convective precipitating regions. The downscaled results performed as well as the CMORPH product over regions dominated with frontal rain systems and performed relatively poorly over mountainous or hilly areas where orographic rain systems dominate.

scholarly journals Evaluation of IMERG Level-3 Products in Depicting the July to October Rainfall over Taiwan: Typhoon Versus Non-Typhoon

2021 ◽  
Vol 13 (4) ◽  
pp. 622
Author(s):  
Wan-Ru Huang ◽  
Pin-Yi Liu ◽  
Ya-Hui Chang ◽  
Cheng-An Lee
Keyword(s):  
Real Time ◽  
Tropical Rainfall Measuring Mission ◽  
Temporal Variations ◽  
Rain Gauge ◽  
Advantages And Disadvantages ◽  
Level 3 ◽  
Spatio Temporal ◽  
Typhoon Season ◽  
Early Late ◽  
Better Than

This study assesses the performance of satellite precipitation products (SPPs) from the latest version, V06B, Integrated Multi-satellitE Retrievals for Global Precipitation Mission (IMERG) Level-3 (including early, late, and final runs), in depicting the characteristics of typhoon season (July to October) rainfall over Taiwan within the period of 2000–2018. The early and late runs are near-real-time SPPs, while final run is post-real-time SPP adjusted by monthly rain gauge data. The latency of early, late, and final runs is approximately 4 h, 14 h, and 3.5 months, respectively, after the observation. Analyses focus on the seasonal mean, daily variation, and interannual variation of typhoon-related (TC) and non-typhoon-related (non-TC) rainfall. Using local rain-gauge observations as a reference for evaluation, our results show that all IMERG products capture the spatio-temporal variations of TC rainfall better than those of non-TC rainfall. Among SPPs, the final run performs better than the late run, which is slightly better than the early run for most of the features assessed for both TC and non-TC rainfall. Despite these differences, all IMERG products outperform the frequently used Tropical Rainfall Measuring Mission 3B42 v7 (TRMM7) for the illustration of the spatio-temporal characteristics of TC rainfall in Taiwan. In contrast, for the non-TC rainfall, the final run performs notably better relative to TRMM7, while the early and late runs showed only slight improvement. These findings highlight the advantages and disadvantages of using IMERG products for studying or monitoring typhoon season rainfall in Taiwan.

scholarly journals Comparison of Entropy Methods for an Optimal Rain Gauge Network: A Case Study of Daegu and Gyeongbuk Area in South Korea

2020 ◽  
Vol 10 (16) ◽  
pp. 5620
Author(s):  
Taeyong Kwon ◽  
Junghyun Lim ◽  
Seongsim Yoon ◽  
Sanghoo Yoon
Keyword(s):  
Conditional Entropy ◽  
Rain Gauge ◽  
Optimal Number ◽  
Severe Drought ◽  
Prediction Errors ◽  
Joint Entropy ◽  
Spatio Temporal ◽  
Rain Gauge Network ◽  
Better Than

To reduce hydrological disasters, it is necessary to operate rain gauge stations at locations where the spatio-temporal characteristics of rainfall can be reflected. Entropy has been widely used to evaluate the designs and uncertainties associated with rain gauge networks. In this study, the optimal rain gauge network in the Daegu and Gyeongbuk area, which requires the efficient use of water resources due to low annual precipitation and severe drought damage, was determined using conditional and joint entropy, and the selected network was quantitatively evaluated using the root mean square error (RMSE). To consider spatial distribution, prediction errors were generated using kriging. Four estimators used in entropy calculations were compared, and weighted entropy was calculated by weighting the precipitation. The optimal number of rain gauge stations was determined by calculating the RMSE reduction and the reduction ratio according to the number of selected rain gauge stations. Our findings show that the results of conditional entropy were better than those of joint entropy. The optimal rain gauge stations showed a tendency wherein peripheral rain gauge stations were selected first, with central stations being added afterward.

Spatial and temporal evaluation of multiple gridded precipitation datasets over complex topography and variable climate of Turkey

2021 ◽  
Author(s):  
Gokcen Uysal ◽  
Hamed Hafizi ◽  
Ali Arda Sorman
Keyword(s):  
Poor Performance ◽  
Temporal Consistency ◽  
Precipitation Data ◽  
Precipitation Estimates ◽  
Dry Seasons ◽  
Spatio Temporal ◽  
Autumn And Winter ◽  
Development And Management ◽  
Precipitation Events ◽  
Better Than

<p>Evaluation of problems related to water resources development and management require accurate precipitation estimates. Although ground-based stations provide direct physical measurement of precipitation, the accuracy of gauge-based precipitation data in terms of quality and spatial pattern may still be controversial. On the other hand, Gridded Precipitation Datasets (GPDs) provide high spatial and temporal precipitation estimates. GPDs are continuously changing with the improving technology and updating of retrospective algorithms, but they still need to be assessed over different regions both in space and time before being used for hydro-climatic studies. This study attempts to evaluate the spatio-temporal consistency of 13 different GPDs (CPCv1, MSWEPv2.2, ERA5, CHIRPSv2.0, CHIRPv2.0, IMERGHHFv06, IMERGHHEv06, IMERGHHLv06, TMPA-3b42v07, TMPA-3b42RTv07, PERSIANN-CDR, PERSIANN-CCS and PERSIANN) over Turkey which is a country characterized by diverse climate and complex terrain. The evaluation is performed for daily and monthly time scales considering the entire period of 2015-2019 as well as seasonal (spring, summer, autumn and winter) variability. Precipitation data from 130 stations are provided as reference data for point-to-grid comparison of GPDs. The modified Kling Gupta Efficiency (KGE) is selected for qualitative analysis whereas the Hanssen–Kuipers Score (HKS) is used to identify the ability of GPDs for capturing various precipitation events. The Probability Density Function (PDF) is selected to evaluate the intensity frequency of 13 GPDs for individual daily-based precipitation events. The results indicate that all GPDs have a median KGE performance ranging between -0.11 and 0.53 for daily precipitation while their performance increases in the monthly case (median KGE from 0.16 to 0.82). Gauge-corrected GPDs exhibit slightly better results over the uncorrected datasets in comparison with ground observations. GPDs from multi-source merging perform better than only satellite-based and reanalysis precipitation datasets. Among uncorrected GPDs, ERA5 and CHIRPv2.0 perform better while PERSIANN perform worse in all conditions. MSWEPv2.2 suffers from high-altitude conditions during winter and CHIRPSv2.0 shows poor performance during dry seasons. On the overall, MSWEPv2.2 performs better than CHIRPSv2.0 during daily/monthly, while CHIRPv2.0 performs better than CHIRPSv2.0 for daily time scale.</p>

scholarly journals Validation of the Accuracy of Different Precipitation Datasets over Tianshan Mountainous Area

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Chuancheng Zhao ◽  
Shuxia Yao ◽  
Shiqiang Zhang ◽  
Haidong Han ◽  
Qiudong Zhao ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Satellite Remote Sensing ◽  
Large Scale ◽  
Regional Scale ◽  
Precipitation Amount ◽  
Rain Gauge ◽  
Small Scale ◽  
Accurate Information ◽  
Mountainous Area ◽  
Basin Scale

Precipitation is one of the important water supplies in the arid and semiarid regions of northwestern China, playing a vital role in maintaining the fragile ecosystem. In remote mountainous area, it is difficult to obtain an accurate and reliable spatialization of the precipitation amount at the regional scale due to the inaccessibility, the sparsity of observation stations, and the complexity of relationships between precipitation and topography. Furthermore, accurate precipitation is important driven data for hydrological models to assess the water balance and water resource for hydrologists. Therefore, the use of satellite remote sensing becomes an important means over mountainous area. Precipitation datasets based on station data or pure satellite data have been increasingly available in spite of several weaknesses. This paper evaluates the usefulness of three precipitation datasets including TRMM 3B43_V6, 3B43_V7, and Asian Precipitation Highly Resolved Observational Data Integration Towards Evaluation with rain gauge data over Tianshan mountainous area where precipitation data is scarce. The results suggest that precipitation measurements only provided accurate information on a small scale, while the satellite remote sensing of precipitation had obvious advantages in basin scale or large scale especially over remote mountainous area.

scholarly journals From regional climate simulations to the hydrological information needed for basin scale impact studies

2010 ◽  
Vol 26 ◽  
pp. 25-31
Author(s):  
I. Portoghese ◽  
E. Bruno ◽  
M. Vurro
Keyword(s):  
Climate Models ◽  
Climate Model ◽  
Regional Climate ◽  
Daily Rainfall ◽  
Climate Change Impacts ◽  
Stochastic Modelling ◽  
Rain Gauge ◽  
Hydrological Processes ◽  
Rainfall Regime ◽  
Basin Scale

Abstract. The accuracy of local downscaling of rainfall predictions provided by climate models is crucial for the assessment of climate change impacts on hydrological processes because the presence of bias in downscaled precipitation may produce large bias in the assessment of soil moisture dynamics, river flows, and groundwater recharge. In this study, the output of a regional climate model (RCM) is downscaled using a stochastic modelling of the point rainfall process able to adequately reproduce the daily rainfall intermittency which is one of the crucial aspects for the hydrological processes characterizing Mediterranean environments. The historical time-series from a dense rain-gauge network were used for the analysis of the RCM bias in terms of dry and wet daily period and then to investigate the predicted alteration in the local rainfall regime. A Poisson Rectangular Pulse (PRP) model (Rodriguez-Iturbe et al., 1987) was finally adopted for the stochastic generation of local daily rainfall as a continuous-time point process with forcing parameters resulting from the bias correction of the RCM scenario.

scholarly journals Drought Hazard Mapping from Regional Climate Multimodel Ensemble over Spain

2015 ◽  
Vol 10 (2) ◽  
pp. 17
Author(s):  
Sandra G. Garcia Galiano ◽  
Juan Diego Giraldo Osorio ◽  
Patricia Olmos Gimenez
Keyword(s):  
Time Series ◽  
Goodness Of Fit ◽  
Regional Climate ◽  
Distribution Functions ◽  
Cumulative Distribution ◽  
Hazard Mapping ◽  
Annual Maximum ◽  
Dry Spells ◽  
Basin Scale ◽  
Spatio Temporal

<p>Improving the knowledge about the impacts of climate change on extreme drought events at basin scale, is important for decision makers in order to develop drought contingency plans which are the leading edge of adaptive management strategy. Considering high-resolution grids of observed daily rainfall and information provided by latest-generation Regional Climate Models (RCMs), the changes in the spatio-temporal patterns of extreme droughts in peninsular Spain are assessed. The non-stationarity character of time series, due to climate and anthropogenic changes, is represented by probabilistic models considering the time evolution of probability density function (PDF) parameters fitted to annual maximum lengths of dry spells time series. By a PDF ensemble from 17 RCMs, the spatio-temporal variability exhibited by the RCMs is represented. Scoring of models is based in the goodness-of-fit to CDFs (cumulative distribution functions) of observed annual maximum dry spells lengths. The reliability and skills of RCMs are assessed, for building the PDF ensemble, at grid site for the study area. Therefore, by adjusting PDF to series of annual maximum dry spells lengths, applying GAMLSS and bootstrapping techniques, the assessment of regional changes and trends associated to high returns periods (<em>Tr</em> = 25 and 50 yr.) is assessed. In general, an intensification of drought events for 2050 horizon, in contrast with 1990, is expected. By increasing return periods, the length of the annual maximum dry spells rises, albeit with a smaller number of areas with significant differences. The areas prone to extreme droughts in mainland Spain are identified.</p>

scholarly journals Spatio-temporal trends in the hydroclimate of Turkey for the last decades based on two reanalysis datasets

2016 ◽  
Author(s):  
Mustafa Gokmen
Keyword(s):  
Snow Water Equivalent ◽  
Water Cycle ◽  
Eastern Mediterranean ◽  
Regional Scale ◽  
Temporal Trends ◽  
Aegean Sea ◽  
Rain Gauge ◽  
Observation Data ◽  
Spatiotemporal Trends ◽  
Snow Water

Abstract. We present a regional assessment of the spatiotemporal trends in several hydro-climate variables from 1979 to 2010 in Turkey, one of the vulnerable countries of the Eastern Mediterranean to climate change, using the two reanalysis products of ECMWF: ERA-Interim and ERA-Interim/Land, namely. The trend analysis revealed that an average warming of 1.26 °C occurred in Turkey from 1979 to 2010, with high confidence intervals (95 to 99 %) mostly. Geographically, the largest warming (up to 1.8 °C) occurred in the Western coastal areas next to Aegean Sea and in the South-East regions. The increasing trend of air temperature was confirmed by the comparisons with the measurements from several meteorological stations. With respect to the regional trends in hydrological variables, ERA-Interim and ERA-Interim/Land revealed quite different pictures: the ERA-Interim dataset indicated that there have been significant decreasing trends of precipitation, snow water equivalent and runoff in some parts of inner/South-eastern Anatolia (up to 250 mm decrease totally in the upstream of Euphrates, Kizilirmak and Seyhan basins), while ERA-Interim/Land showed none or minor trends in the same areas. Comparison of the precipitation trends by the two datasets with some rain gauge data distributed over Turkey revealed that none of the products is consistently closer to the observations. Based on the trend assessment of the hydrological trends by the two datasets and the comparisons with the observation data and other trend studies in the study area we can conclude that, except for some evapotranspiration trends over Mediterranean and Black Sea, there have not been clear and considerable trends of precipitation, snow water equivalent and runoff quantities over Turkey from 1979 to 2010, despite the considerable warming for the same period throughout the country. In this respect, we can suggest that, the impacts of global warming on the water cycle are rather unpredictable especially at regional scale.

Evaluation of the TRMM Product for Spatio-temporal Characteristics of Precipitation over Nepal (1998-2018)

2020 ◽  
Vol 25 (2) ◽  
pp. 39-48
Author(s):  
Kalpana Hamal ◽  
Nitesh Khadka ◽  
Samresh Rai ◽  
Bharat Badayar Joshi ◽  
Jagdish Dotel ◽  
...  
Keyword(s):  
Water Cycle ◽  
Winter Season ◽  
Temporal Distribution ◽  
Tropical Rainfall Measuring Mission ◽  
Good Alternative ◽  
High Elevation ◽  
Large Error ◽  
Rain Gauge ◽  
Mean Annual Precipitation ◽  
Spatio Temporal

Precipitation is a fundamental component of the water cycle and integral to the society and the ecosystem. Further, continuous monitoring of precipitation is essential for predicting severe weather, monitoring droughts, and high-intensity related extremes. The present study evaluated the spatio-temporal distribution of precipitation and trends between 1998– 2018 using Tropical Rainfall Measuring Mission (TRMM) (3B43-V7) with reference to 142-gauge observations over Nepal. TRMM moderately captured precipitation patterns' overall characteristics, although underestimated the mean annual precipitation during the study period. TRMM precipitation product well captured the seasonal variation of the observed precipitation with the highest correlation in the winter season. The decreasing seasonal and annual trend was found in both observed and TRMM products, with the highest (lowest) decreasing trend observed during the monsoon (winter) season. It was also noted that the TRMM product showed a smaller bias before 2007, while a large error was found after 2007, especially in the monsoon months. In general, the TRMM product is a good alternative to observe rain gauge measurement in Nepal. However, there is still space for further improvement in rainfall retrieval algorithms, especially in high-elevation areas during the winter season.

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