scholarly journals Mitigating Spatial Discontinuity of Multi-Radar QPE Based on GPM/KuPR

Hydrology ◽  
2018 ◽  
Vol 5 (3) ◽  
pp. 48 ◽  
Author(s):  
Zhigang Chu ◽  
Yingzhao Ma ◽  
Guifu Zhang ◽  
Zhenhui Wang ◽  
Jing Han ◽  
...  
Keyword(s):  
Absolute Error ◽  
Calibration Method ◽  
Random Errors ◽  
Spatial Continuity ◽  
Precipitation Measurement ◽  
Precipitation Estimation ◽  
Radar Calibration ◽  
Spatial Discontinuity ◽  
Global Precipitation ◽  
Reflectivity Factor

Reflectivity factor bias caused by radar calibration errors would influence the accuracy of Quantitative Precipitation Estimations (QPE), and further result in spatial discontinuity in Multiple Ground Radars QPE (MGR-QPE) products. Due to sampling differences and random errors, the associated discontinuity cannot be thoroughly solved by the single-radar calibration method. Thus, a multiple-radar synchronous calibration approach was proposed to mitigate the spatial discontinuity of MGR-QPE. Firstly, spatial discontinuity was solved by the intercalibration of adjacent ground radars, and then calibration errors were reduced by referring to the Ku-Band Precipitation Radar (KuPR) carried by the Global Precipitation Measurement (GPM) Core Observatory as a standard reference. Finally, Mosaic Reflectivity and MGR-QPE products with spatial continuity were obtained. Using three S-band operational radars covering the lower reaches of the Yangtze River in China, this method was evaluated under four representative precipitation events. The result showed that: (1) the spatial continuity of reflectivity factor and precipitation estimation fields was significantly improved after bias correction, and the reflectivity differences between adjacent radars were reduced by 78% and 82%, respectively; (2) the MGR-QPE data were closer to gauge observations with the normalized absolute error reducing by 0.05 to 0.12.

scholarly journals Deep Learning-Based Radar Composite Reflectivity Factor Estimations from Fengyun-4A Geostationary Satellite Observations

2021 ◽  
Vol 13 (11) ◽  
pp. 2229
Author(s):  
Fenglin Sun ◽  
Bo Li ◽  
Min Min ◽  
Danyu Qin
Keyword(s):  
Deep Learning ◽  
Absolute Error ◽  
Radar Data ◽  
Geostationary Satellite ◽  
Precipitation Pattern ◽  
Mountainous Regions ◽  
Precipitation Measurement ◽  
Spatial Coverage ◽  
Global Precipitation ◽  
Reflectivity Factor

Ground-based weather radar data plays an essential role in monitoring severe convective weather. The detection of such weather systems in time is critical for saving people’s lives and property. However, the limited spatial coverage of radars over the ocean and mountainous regions greatly limits their effective application. In this study, we propose a novel framework of a deep learning-based model to retrieve the radar composite reflectivity factor (RCRF) maps from the Fengyun-4A new-generation geostationary satellite data. The suggested framework consists of three main processes, i.e., satellite and radar data preprocessing, the deep learning-based regression model for retrieving the RCRF maps, as well as the testing and validation of the model. In addition, three typical cases are also analyzed and studied, including a cluster of rapidly developing convective cells, a Northeast China cold vortex, and the Super Typhoon Haishen. Compared with the high-quality precipitation rate products from the integrated Multi-satellite Retrievals for Global Precipitation Measurement, it is found that the retrieved RCRF maps are in good agreement with the precipitation pattern. The statistical results show that retrieved RCRF maps have an R-square of 0.88-0.96, a mean absolute error of 0.3-0.6 dBZ, and a root-mean-square error of 1.2-2.4 dBZ.

scholarly journals Validation of rainfall data estimated by GPM satellite on Southern Amazon region

2019 ◽  
Vol 14 (1) ◽  
pp. 1
Author(s):  
Luiz Octavio Fabricio dos Santos ◽  
Carlos Alexandre Santos Querino ◽  
Juliane Kayse Albuquerque da Silva Querino ◽  
Altemar Lopes Pedreira Junior ◽  
Aryanne Resende de Melo Moura ◽  
...  
Keyword(s):  
Pearson Correlation ◽  
Absolute Error ◽  
High Rate ◽  
Satellite Precipitation ◽  
Precipitation Measurement ◽  
Surface Data ◽  
Precipitation Estimates ◽  
Amazonas State ◽  
Global Precipitation Measurement ◽  
Global Precipitation

Rainfall is a meteorological variable of great importance for hydric balance and for weather studies. Rainfall estimation, carried out by satellites, has increased the climatological dataset related to precipitation. However, the accuracy of these data is questionable. This paper aimed to validate the estimates done by the Global Precipitation Measurement (GPM) satellite for the mesoregion of Southern Amazonas State, Brazil. The surface data were collected by the National Water Agency – ANA and National Institute of Meteorology – INMET, and is available at both institutions’ websites. The satellite precipitation data were accessed directly from the NASA webpage. Statistical analysis of Pearson correlation was used, as well as the Willmott’s “d” index and errors from the MAE (Mean Absolute Error) and RMSE (Root Mean Square Error). The GPM satellite satisfactorily estimated the precipitation, once it had correlations above 73% and high Willmott coefficients (between 0.86 and 0.97). The MAE and RMSE showed values that varied from 36.50 mm to 72.49 mm and 13.81 mm to 71.76 mm, respectively. Seasonal rain variations are represented accordingly. In some cases, either an underestimation or an overestimation of the rain data was observed. In the yearly totals, a high rate of similarity between the estimated and measured values was observed. We concluded that the GPM-based multi-satellite precipitation estimates can be used, even though they are not 100% reliable. However, adjustments in calibration for the region are necessary and recommended.

scholarly journals The Summertime Diurnal Cycle of Precipitation Derived from IMERG

2019 ◽  
Vol 11 (15) ◽  
pp. 1781 ◽  
Author(s):  
Daniel Watters ◽  
Alessandro Battaglia
Keyword(s):  
Diurnal Cycle ◽  
Coastal Waters ◽  
Least Squares Method ◽  
Random Errors ◽  
Diurnal Cycles ◽  
Precipitation Measurement ◽  
Diurnal Cycle Of Precipitation ◽  
Global Precipitation Measurement ◽  
Global Precipitation ◽  
Latitudinal Band

The Integrated Multi-satellitE Retrievals for GPM (IMERG) precipitation product derived from the Global Precipitation Measurement (GPM) constellation offers a unique opportunity of observing the diurnal cycle of precipitation in the latitudinal band 60 ° N–S at unprecedented 0.1 ° × 0.1 ° and half-hour resolution. The diurnal cycles of occurrence, intensity and accumulation are determined using four years of data at 2 ° × 2 ° resolution; this study focusses on summertime months when the diurnal cycle shows stronger features. Harmonics are fitted to the diurnal cycle using a non-linear least squares method weighted by random errors. Results suggest that mean-to-peak amplitudes for the diurnal cycles of occurrence and accumulation are greater over land (generally larger than 25% of the diurnal mean), where the diurnal harmonic dominates and peaks at ~16–24 LST, than over ocean (generally smaller than 25%), where the diurnal and semi-diurnal harmonics contribute comparably. Over ocean, the diurnal harmonic peaks at ~0–10 LST (~8–15 LST) over open waters (coastal waters). For intensity, amplitudes of the diurnal and semi-diurnal harmonics are generally comparable everywhere (~15–35%) with the diurnal harmonic peaking at ~20–4 LST (~3–12 LST) over land (ocean), and the semi-diurnal harmonic maximises at ~5–8 LST and 17–20 LST. The diurnal cycle of accumulation is dictated by occurrence as opposed to intensity.

Improving Precipitation Retrieval by Brightness Temperature Temporal Variation (ΔTB): Definition, Computation, and Application

2021 ◽  
Author(s):  
Yalei You ◽  
Christa Peters-Lidard ◽  
Stephen Munchak ◽  
Sarah Ringerud
Keyword(s):  
Temporal Variation ◽  
Brightness Temperature ◽  
Land Surface ◽  
Microwave Radiometer ◽  
Daily Rainfall ◽  
Surface Emissivity ◽  
Time Dimension ◽  
Precipitation Measurement ◽  
Precipitation Estimation ◽  
Global Precipitation

<p>Current microwave precipitation retrieval algorithms utilize the instantaneous brightness temperature (TB) from a single satellite to estimate the precipitation rate. This study proposed to add the time-dimension into the precipitation estimation process by using the TB (or emissivity) temporal variation (ΔTB or Δe) derived from the Global Precipitation Measurement (GPM) microwave radiometer constellation.  Results showed that (1) ΔTB can improve the precipitation estimation over the cold surfaces (i.e., snow-covered region) through minimizing the microwave land surface emissivity’s influence; (2) Δe under the clear-sky conditions can accurately estimate the daily rainfall accumulation; and (3) ΔTB can be used to identify the liquid raindrop signature over the low surface emissivity areas. This study highlights the importance of maintaining the current passive microwave satellite constellation.</p>

The Global Precipitation Climatology Project Version 3 Products

2021 ◽  
Author(s):  
George J. Huffman ◽  
Ali Behrangi ◽  
Robert F. Adler ◽  
David T. Bolvin ◽  
Eric J. Nelkin ◽  
...  
Keyword(s):  
Global Precipitation Climatology Project ◽  
Climate Data ◽  
Precipitation Climatology ◽  
Global Precipitation Climatology Centre ◽  
Early Results ◽  
Precipitation Measurement ◽  
Key Points ◽  
Precipitation Estimation ◽  
Global Precipitation Measurement ◽  
Global Precipitation

<p>The Global Precipitation Climatology Project (GPCP) is currently providing a next-generation Version 3.1 Monthly product, which covers the period 1983-2019.  This modernized product includes higher spatial resolution (0.5°x0.5°); a wider coverage (60°N-S) by geosynchronous IR estimates, now based on the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR) algorithm, with monthly recalibration using Goddard Profiling (GPROF) algorithm retrievals from selected passive microwave sensors; and improved calibrations of Television-Infrared Operational Satellite (TIROS) Operational Vertical Sounder (TOVS) and Advanced Infrared Sounder (AIRS) precipitation, used outside 60ºN-S.  The merged satellite estimate is adjusted to the Tropical Combined Climatology (TCC) at lower latitudes, and the Merged CloudSat, TRMM, and GPM (MCTG) climatology at higher latitudes.  Finally, V3.1 provides a merger of the satellite-only estimates with the Global Precipitation Climatology Centre (GPCC) monthly 1°x1° gauge analyses. </p><p>As well, the GPCP team is advancing a companion global Version 3 Daily product, in which the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (GPM) mission (IMERG) Final Run V06 estimates are used where available (initially restricted to 60°N-S), and rescaled TOVS/AIRS data in high-latitude areas, all calibrated to the GPCP V3.1 Monthly estimate.  Since IMERG currently extends back to June 2000, daily PERSIANN-CDR data will be used for the period January 1983–May 2000 to complete the record.</p><p>This presentation will provide early results for, and the latest status of, the Monthly and Daily GPCP products as a function of time and region.  Key points include examining homogeneity over time and across time and space boundaries between input datasets.  One key activity is to refine the V3 products while we continue to produce the Version 2 GPCP products for on-going use.</p>

Satellite-Based Precipitation Estimation and Its Application for Streamflow Prediction over Mountainous Western U.S. Basins

2014 ◽  
Vol 53 (12) ◽  
pp. 2823-2842 ◽  
Author(s):  
Ali Behrangi ◽  
Konstantinos Andreadis ◽  
Joshua B. Fisher ◽  
F. Joseph Turk ◽  
Stephanie Granger ◽  
...  
Keyword(s):  
Tropical Rainfall Measuring Mission ◽  
Current Status ◽  
Streamflow Prediction ◽  
Infrared Sensors ◽  
Infiltration Capacity ◽  
Mountainous Regions ◽  
Earth Observing System ◽  
Precipitation Measurement ◽  
Precipitation Estimation ◽  
Global Precipitation

AbstractRecognizing the importance and challenges inherent to the remote sensing of precipitation in mountainous areas, this study investigates the performance of the commonly used satellite-based high-resolution precipitation products (HRPPs) over several basins in the mountainous western United States. Five HRPPs [Tropical Rainfall Measuring Mission 3B42 and 3B42-RT algorithms, the Climate Prediction Center morphing technique (CMORPH), Precipitation Estimation from Remotely Sensed Imagery Using Artificial Neural Networks (PERSIANN), and the PERSIANN Cloud Classification System (PERSIANN-CCS)] are analyzed in the present work using ground gauge, gauge-adjusted radar, and CloudSat precipitation products. Using ground observation of precipitation and streamflow, the skill of HRPPs and the resulting streamflow simulations from the Variable Infiltration Capacity hydrological model are cross-compared. HRPPs often capture major precipitation events but seldom capture the observed magnitude of precipitation over the studied region and period (2003–09). Bias adjustment is found to be effective in enhancing the HRPPs and resulting streamflow simulations. However, if not bias adjusted using gauges, errors are typically large as in the lower-level precipitation inputs to HRPPs. The results using collocated Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) and CloudSat precipitation data show that missing data, often over frozen land, and limitations in retrieving precipitation from systems that lack frozen hydrometeors contribute to the observed microwave-based precipitation errors transferred to HRPPs. Over frozen land, precipitation retrievals from infrared sensors and microwave sounders show some skill in capturing the observed precipitation climatology maps. However, infrared techniques often show poor detection skill, and microwave sounding in dry atmosphere remains challenging. By recognizing the sources of precipitation error and in light of the operation of the Global Precipitation Measurement mission, further opportunity for enhancing the current status of precipitation retrievals and the hydrology of cold and mountainous regions becomes available.

scholarly journals An Efficient Downscaling Scheme for High-Resolution Precipitation Estimates over a High Mountainous Watershed

2021 ◽  
Vol 13 (2) ◽  
pp. 234
Author(s):  
Na Zhao
Keyword(s):  
High Resolution ◽  
Model Performance ◽  
Absolute Error ◽  
Rain Gauge ◽  
Monthly Precipitation ◽  
Precipitation Measurement ◽  
Precipitation Estimates ◽  
Residual Correction ◽  
Global Precipitation Measurement ◽  
Global Precipitation

Satellites are capable of observing precipitation over large areas and are particularly suitable for estimating precipitation in high mountains and poorly gauged regions. However, the coarse resolution and relatively low accuracy of satellites limit their applications. In this study, a downscaling scheme was developed to obtain precipitation estimates with high resolution and high accuracy in the Heihe watershed. Shannon’s entropy, together with a semi-variogram, was applied to establish the optimal precipitation station network. A combination of the random forest (RF) method and the residual correction approach with the established rain gauge network was applied to downscale monthly precipitation products from Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG). The results indicated that the RF model showed little improvement in the accuracy of IMERG-based precipitation downscaling. Including residual modification could improve the results of the RF model. The mean absolute error (MAE) and root mean square error (RMSE) values decreased by 19% and 21%, respectively, after residual corrections were added to the RF approach. Moreover, we found that enough rain gauge records are necessary for and remain an important component of tuning model performance. The application of more rain gauges improves the performance of the combined RF and residual modification methods, with the MAE and RMSE values reduced by 8% and 9%, respectively. Residual correction, together with enough precipitation stations, can effectively enhance the quality of the precipitation patterns and magnitudes obtained in the RF downscaling process. The proposed downscaling scheme is an effective tool for increasing the accuracy and spatial resolution of precipitation fields in the Heihe watershed.

scholarly journals Hydrometeorological Assessment of Satellite and Model Precipitation Products over Taiwan

2021 ◽  
Author(s):  
Pin-Lun Li ◽  
Chia-Jeng Chen ◽  
Liao-Fan Lin
Keyword(s):  
Extreme Event ◽  
Wrf Model ◽  
Warm Season ◽  
Flow Simulation ◽  
Cold Season ◽  
Regional Variations ◽  
Precipitation Measurement ◽  
Precipitation Estimation ◽  
Global Precipitation ◽  
Model Precipitation

AbstractSatellite and model precipitation such as the Global Precipitation Measurement (GPM) data are valuable in hydrometeorological applications. This study investigates the performance of various satellite and model precipitation products in Taiwan from 2015 to 2017, including data derived from the Integrated Multi-satellitE Retrievals for GPM Early and Final Runs (IMERG_E and IMERG_F), Global Satellite Mapping of Precipitation_near-real-time (GSMaP_NRT), and the Weather Research and Forecasting (WRF) model. We assess these products by comparing them against data collected from 304 surface stations and gauge-based gridded data. Our assessment emphasizes factors influential in precipitation estimation, such as season, temperature, elevation, and extreme event. Further, we assess the hydrological response to each precipitation product via continuous flow simulation in two selected watersheds. The results indicate that the performance of these precipitation products is subject to seasonal and regional variations. The satellite products (i.e., IMERG and GSMaP) perform better than the model (i.e., WRF) in the warm season and vice versa in the cold season, most apparently in northern Taiwan. For selected extreme events, WRF can simulate better rainfall amount and distribution. The seasonal and regional variations in precipitation estimation are also reflected in flow simulation: IMERG in general produces the most rational flow simulation, GSMaP tends to overestimate and be least useful for hydrological applications, while WRF simulates high flows that show accurate time to the peak flows and are better in the southern watershed.

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