scholarly journals Summer Precipitation Frequency, Intensity, and Diurnal Cycle over China: A Comparison of Satellite Data with Rain Gauge Observations

2008 ◽  
Vol 21 (16) ◽  
pp. 3997-4010 ◽  
Author(s):  
Tianjun Zhou ◽  
Rucong Yu ◽  
Haoming Chen ◽  
Aiguo Dai ◽  
Yang Pan
Keyword(s):  
Eastern China ◽  
Precipitation Amount ◽  
Rain Gauge ◽  
Rainfall Amount ◽  
The Tibetan Plateau ◽  
Precipitation Frequency ◽  
Rain Gauge Data ◽  
Late Afternoon ◽  
Pattern Correlation ◽  
Gauge Data

Abstract Hourly or 3-hourly precipitation data from Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) and Tropical Rainfall Measuring Mission (TRMM) 3B42 satellite products and rain gauge records are used to characterize East Asian summer monsoon rainfall, including spatial patterns in June–August (JJA) mean precipitation amount, frequency, and intensity, as well as the diurnal and semidiurnal cycles. The results show that the satellite products are comparable to rain gauge data in revealing the spatial patterns of JJA precipitation amount, frequency, and intensity, with pattern correlation coefficients for five subregions ranging from 0.66 to 0.94. The pattern correlation of rainfall amount is higher than that of frequency and intensity. Relative to PERSIANN, the TRMM product has a better resemblance with rain gauge observations in terms of both the pattern correlation and root-mean-square error. The satellite products overestimate rainfall frequency but underestimate its intensity. The diurnal (24 h) harmonic dominates subdaily variations of precipitation over most of eastern China. A late-afternoon maximum over southeastern and northeastern China and a near-midnight maximum over the eastern periphery of the Tibetan Plateau are seen in the rain gauge data. The diurnal phases of precipitation frequency and intensity are similar to those of rainfall amount in most regions, except for the middle Yangtze River valley. Both frequency and intensity contribute to the diurnal variation of rainfall amount over most of eastern China. The contribution of frequency to the diurnal cycle of rainfall amount is generally overestimated in both satellite products. Both satellite products capture well the nocturnal peak over the eastern periphery of the Tibetan Plateau and the late-afternoon peak in southern and northeastern China. Rain gauge data over the region between the Yangtze and Yellow Rivers show two peaks, with one in the early morning and the other later in the afternoon. The satellite products only capture the major late-afternoon peak.

Regimes of Diurnal Variation of Summer Rainfall over Subtropical East Asia

2012 ◽  
Vol 25 (9) ◽  
pp. 3307-3320 ◽  
Author(s):  
Weihua Yuan ◽  
Rucong Yu ◽  
Minghua Zhang ◽  
Wuyin Lin ◽  
Haoming Chen ◽  
...  
Keyword(s):  
Diurnal Variation ◽  
East Asia ◽  
Hydrological Cycle ◽  
Eastern China ◽  
Summer Rainfall ◽  
Early Morning ◽  
Rain Gauge ◽  
Western China ◽  
The Tibetan Plateau ◽  
Late Afternoon

Using hourly rain gauge records and Tropical Rainfall Measuring Mission 3B42 from 1998 to 2006, the authors present an analysis of the diurnal characteristics of summer rainfall over subtropical East Asia. The study shows that there are four different regimes of distinct diurnal variation of rainfall in both the rain gauge and the satellite data. They are located over the Tibetan Plateau with late-afternoon and midnight peaks, in the western China plain with midnight to early-morning peaks, in the eastern China plain with double peaks in late afternoon and early morning, and over the East China Sea with an early-morning peak. No propagation of diurnal phases is found from the land to the ocean across the coastlines. The different diurnal regimes are highly correlated with the inhomogeneous underlying surface, such as the plateau, plain, and ocean, with physical mechanisms consistent with the large-scale “mountain–valley” and “land–sea” breezes and convective instability. These diurnal characteristics over subtropical East Asia can be used as diagnostic metrics to evaluate the physical parameterization and hydrological cycle of climate models over East Asia.

scholarly journals Coastal and orographic effects on extreme precipitation revealed by weather radar observations

2021 ◽  
Author(s):  
Francesco Marra ◽  
Moshe Armon ◽  
Efrat Morin
Keyword(s):  
Extreme Precipitation ◽  
Weather Radar ◽  
Radar Data ◽  
Rain Gauge ◽  
Exceedance Probability ◽  
Precipitation Frequency ◽  
Rain Gauge Data ◽  
Orographic Effects ◽  
Gauge Data ◽  
Convective Cells

Abstract. The yearly exceedance probability of extreme precipitation of multiple durations is crucial for infrastructure design, risk management and policymaking. Local extremes emerge from the interaction of weather systems with local terrain features such as coastlines and orography, however multi-duration extremes do not follow exactly the patterns of cumulative precipitation and are still not well understood. High-resolution information from weather radars could help us better quantifying their patterns, but traditional extreme-value analyses based on radar records were found too inaccurate for quantifying the extreme intensities for impact studies. Here, we propose a novel methodology for extreme precipitation frequency analysis based on relatively short weather radar records, and we use it to investigate coastal and orographic effects on extreme precipitation of durations between 10 minutes and 24 hours. Combining 11 years of radar data with 10-minute rain gauge data in the southeastern Mediterranean, we obtain estimates of the 1 in 100 years intensities with ~22 % standard error, which is lower than those obtained using traditional approaches on rain gauge data. We identify three distinct regimes, which respond differently to coastal and orographic forcing: short durations (~10 minutes), related to peak convective rain rates; hourly durations (~1 hours), related to the yield of individual convective cells; and long durations (~6–24 hours), related to the accumulation of multiple convective cells and to stratiform processes. At short and hourly durations, extreme return levels peak at the coastline, while at longer durations they peak corresponding to the orographic barriers. The distributions tail heaviness is rather uniform above the sea and rapidly changes in presence of orography, with opposing directions at short (decreasing tail heaviness, with a peak at hourly durations) and long (increasing) durations. These distinct effects suggest that short-scale hazards such as urban pluvial floods could be more of concern for the coastal regions, while longer-scale hazards such as flash floods could be more relevant in mountainous areas.

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 Experiences in using the TMPA-3B42R satellite data to complement rain gauge measurements in the Ecuadorian coastal foothills

2013 ◽  
Vol 17 (7) ◽  
pp. 2905-2915 ◽  
Author(s):  
M. Arias-Hidalgo ◽  
B. Bhattacharya ◽  
A. E. Mynett ◽  
A. van Griensven
Keyword(s):  
Satellite Data ◽  
Bias Correction ◽  
Hydrological Modeling ◽  
New Technologies ◽  
Simple Procedure ◽  
Rain Gauge ◽  
Experimental Methodology ◽  
Rain Gauge Data ◽  
Gauge Data ◽  
Satellite Rainfall

Abstract. At present, new technologies are becoming available to extend the coverage of conventional meteorological datasets. An example is the TMPA-3B42R dataset (research – v6). The usefulness of this satellite rainfall product has been investigated in the hydrological modeling of the Vinces River catchment (Ecuadorian lowlands). The initial TMPA-3B42R information exhibited some features of the precipitation spatial pattern (e.g., decreasing southwards and westwards). It showed a remarkable bias compared to the ground-based rainfall values. Several time scales (annual, seasonal, monthly, etc.) were considered for bias correction. High correlations between the TMPA-3B42R and the rain gauge data were still found for the monthly resolution, and accordingly a bias correction at that level was performed. Bias correction factors were calculated, and, adopting a simple procedure, they were spatially distributed to enhance the satellite data. By means of rain gauge hyetographs, the bias-corrected monthly TMPA-3B42R data were disaggregated to daily resolution. These synthetic time series were inserted in a hydrological model to complement the available rain gauge data to assess the model performance. The results were quite comparable with those using only the rain gauge data. Although the model outcomes did not improve remarkably, the contribution of this experimental methodology was that, despite a high bias, the satellite rainfall data could still be corrected for use in rainfall-runoff modeling at catchment and daily level. In absence of rain gauge data, the approach may have the potential to provide useful data at scales larger than the present modeling resolution (e.g., monthly/basin).

scholarly journals On evaluation of ensemble precipitation forecasts with observation-based ensembles

2007 ◽  
Vol 10 ◽  
pp. 139-144 ◽  
Author(s):  
B. Ahrens ◽  
S. Jaun
Keyword(s):  
Rain Gauge ◽  
Performance Measure ◽  
Ensemble Prediction ◽  
Limited Area ◽  
Large Area ◽  
Ensemble Prediction System ◽  
Rain Gauge Data ◽  
Forecast Lead Time ◽  
Gauge Data ◽  
The One

Abstract. Spatial interpolation of precipitation data is uncertain. How important is this uncertainty and how can it be considered in evaluation of high-resolution probabilistic precipitation forecasts? These questions are discussed by experimental evaluation of the COSMO consortium's limited-area ensemble prediction system COSMO-LEPS. The applied performance measure is the often used Brier skill score (BSS). The observational references in the evaluation are (a) analyzed rain gauge data by ordinary Kriging and (b) ensembles of interpolated rain gauge data by stochastic simulation. This permits the consideration of either a deterministic reference (the event is observed or not with 100% certainty) or a probabilistic reference that makes allowance for uncertainties in spatial averaging. The evaluation experiments show that the evaluation uncertainties are substantial even for the large area (41 300 km2) of Switzerland with a mean rain gauge distance as good as 7 km: the one- to three-day precipitation forecasts have skill decreasing with forecast lead time but the one- and two-day forecast performances differ not significantly.

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