scholarly journals Evaluation of Gridded Precipitation Datasets in Malaysia

2020 ◽  
Vol 12 (4) ◽  
pp. 613 ◽  
Author(s):  
Afiqah Bahirah Ayoub ◽  
Fredolin Tangang ◽  
Liew Juneng ◽  
Mou Leong Tan ◽  
Jing Xiang Chung
Keyword(s):  
Heavy Rainfall ◽  
Mean Squared Error ◽  
Tropical Rainfall Measuring Mission ◽  
Heavy Rain ◽  
Rain Gauge ◽  
Peninsular Malaysia ◽  
Probability Of Detection ◽  
Intensity Level ◽  
Critical Success ◽  
Extreme Flooding

This study compares five readily available gridded precipitation satellite products namely: Climate Hazards Group Infrared Precipitation with Station Data (CHIRPS) at 0.05° and 0.25° resolution, Tropical Rainfall Measuring Mission Multi-Satellite Precipitation Analysis (TMPA 3B42v7) and Princeton Global Forcings (PGFv3), both at 0.25°, and Global Satellite Mapping of Precipitation Reanalysis (GSMaP_RNL) at 0.1°, and evaluates their quality and reliability against 41 rain gauge stations in Malaysia. The evaluation was based on three numerical statistical scores (r, Root Mean Squared Error (RMSE) and Bias) and three categorical scores (Probability of Detection (POD), False Alarm Ratio (FAR) and Critical Success Index (CSI)) at temporal resolutions of daily, monthly and seasonal. The results showed that TMPA 3B42v7, PGFv3, CHIRPS25 and CHIRPS05 slightly overestimated the rain gauge data, while the GSMaP_RNL underestimated the value with the largest bias for monthly data. The CHIRPS25 showed the best POD score, while TMPA 3B42v7 scored highest for FAR and CSI. Overall, TMPA 3B42v7 was found to be the best-performing dataset, while PGFv3 registered the worst performance for both for numerical (monthly) and categorical (daily) scores. All products captured the intensity of heavy rainfall (20–50 mm/day) rather well, but tended to underestimate the intensity for categories of no or little rain (rain <1 mm/day) and extremely heavy rain (rain >50 mm/day). In addition, overestimation occurred for low moderate (2–5 mm/day) to low heavy rain and (10–20 mm/day). In the case study of the extreme flooding event of 2006/2007 in the southern area of Peninsular Malaysia, TMPA 3B42v7 and GSMaP_RNL performed well in capturing most heavy rainfall events but tended to overestimate light rainfalls, consistent with their performance for the occurrence intensity of rainfall at different intensity level.

scholarly journals Performance Assessment of GSMaP and GPM IMERG Products during Typhoon Mangkhut

Atmosphere ◽  
2021 ◽  
Vol 12 (2) ◽  
pp. 134
Author(s):  
Xiaoyu Li ◽  
Sheng Chen ◽  
Zhenqing Liang ◽  
Chaoying Huang ◽  
Zhi Li ◽  
...  
Keyword(s):  
Mean Squared Error ◽  
Southern China ◽  
Eastern China ◽  
Mainland China ◽  
Rain Gauge ◽  
Probability Of Detection ◽  
Critical Success ◽  
Rainfall Occurrence ◽  
Precipitation Measurement ◽  
Global Precipitation

This paper evaluated the latest version 6.0 Global Satellite Mapping of Precipitation (GSMaP) and version 6.0 Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG) products during 2018 Typhoon Mangkhut in China. The reference data is the rain gauge datasets from Gauge-Calibrated Climate Prediction Centre (CPC) Morphing Technique (CMORPHGC). The products for comparison include the GSMaP near-real-time, Microwave-IR merged, and gauge-calibrated (GSMaP_NRT, GSMaP_MVK, and GSMaP_Gauge) and the IMERG Early, Final, and Final gauge-calibrated (IMERG_ERUncal, IMERG_FRUncal, and IMERG_FRCal) products. The results show that (1) both GSMaP_Gauge and IMERG_FRCal considerably reduced the bias of their satellite-only products. GSMaP_Gauge outperforms IMERG_FRCal with higher Correlation Coefficient (CC) values of about 0.85, 0.78, and 0.50; lower Fractional Standard Error (FSE) values of about 18.00, 18.85, and 29.30; and Root-Mean-Squared Error (RMSE) values of about 12.12, 33.35, and 32.99 mm in the rainfall centers over mainland China, southern China, and eastern China, respectively. (2) GSMaP products perform better than IMERG products, with higher Probability of Detection (POD) and Critical Success Index (CSI) and lower False Alarm Ratio (FAR) in detecting rainfall occurrence, especially for high rainfall rates. (3) For area-mean rainfall, IMERG performs worse than GSMaP in the rainfall centers over mainland China and southern China but shows better performance in the rainfall center over eastern China. GSMaP_Gauge and IMERG_FRCal perform well in the three regions with a high CC (0.79 vs. 0.94, 0.81 vs. 0.96, and 0.95 vs. 0.97) and a low RMSE (0.04 vs. 0.06, 0.40 vs. 0.59, and 0.19 vs. 0.34 mm). These useful findings will help algorithm developers and data users to better understand the performance of GSMaP and IMERG products during typhoon precipitation events.

scholarly journals Deep Learning for Precipitation Estimation from Satellite and Rain Gauges Measurements

2019 ◽  
Vol 11 (21) ◽  
pp. 2463
Author(s):  
Arthur Moraux ◽  
Steven Dewitte ◽  
Bruno Cornelis ◽  
Adrian Munteanu
Keyword(s):  
Deep Learning ◽  
Multiscale Analysis ◽  
Mean Squared Error ◽  
Rain Gauge ◽  
Probability Of Detection ◽  
Precipitation Rate ◽  
Rain Gauges ◽  
Squared Error ◽  
Precipitation Estimation ◽  
Deep Learning Model

This paper proposes a multimodal and multi-task deep-learning model for instantaneous precipitation rate estimation. Using both thermal infrared satellite radiometer and automatic rain gauge measurements as input, our encoder–decoder convolutional neural network performs a multiscale analysis of these two modalities to estimate simultaneously the rainfall probability and the precipitation rate value. Precipitating pixels are detected with a Probability Of Detection (POD) of 0.75 and a False Alarm Ratio (FAR) of 0.3. Instantaneous precipitation rate is estimated with a Root Mean Squared Error (RMSE) of 1.6 mm/h.

scholarly journals Self-Nowcast Model of extreme precipitation events for operational meteorology

2015 ◽  
Vol 8 (10) ◽  
pp. 10635-10661
Author(s):  
G. B. França ◽  
M. V. de Almeida ◽  
A. C. Rosette
Keyword(s):  
Neural Network ◽  
Heavy Rain ◽  
Local Area ◽  
Rain Gauge ◽  
Probability Of Detection ◽  
Warning Signs ◽  
Short Term ◽  
Validation Data ◽  
Extreme Precipitation Events ◽  
The Impact

Abstract. Nowadays many social activities require short-term (one to two hours) and local area forecasts of extreme weather. In particular, air traffic systems have been studying how to minimize the impact of meteorological events, such as turbulence, wind shear, ice, and heavy rain, which are related to the presence of convective systems during all flight phases. This paper presents an alternative self-nowcast model, based on neural network techniques, to produce short-term and local-specific forecasts of extreme meteorological events in the area of the landing and take-off region of Galeão, the principal airport in Rio de Janeiro, Brazil. Twelve years of data were used for neural network training and validation. Data are originally from four sources: (1) hourly meteorological observations from surface meteorological stations at five airports distributed around the study area, (2) atmospheric profiles collected twice a day at the meteorological station at Galeão Airport, (3) rain rate data collected from a network of twenty-nine rain gauges in the study area; and (4) lightning data regularly collected by national detection networks. An investigation was done about the capability of a neural network to produce early warning signs – or as a nowcasting tool – for extreme meteorological events. The self-nowcast model was validated using results from six categorical statistics, indicated in parentheses for forecasts of the first, second, and third hours, respectively, namely: proportion correct (0.98, 0.96, and 0.94), bias (1.37, 1.48, and 1.83), probability of detection (0.84, 0.80, and 0.76), false-alarm ratio (0.38, 0.46, and 0.58), and threat score (0.54, 0.47, and 0.37). Possible sources of error related to the validation procedure are discussed. Two key points have been identified in which there is a possibility of error: i.e., subjectivity on the part of the meteorologist making the observation, and a rain gauge measurement error of about 20 % depending on wind speed. The latter was better demonstrated when lightning data were included in the validation. The validation showed that the proposed model's performance was quite encouraging for the first and second hours.

scholarly journals Features of weather types involving heavy rainfall along the southern Spanish Mediterranean

2020 ◽  
Author(s):  
J.M. Senciales-González ◽  
J.D. Ruiz-Sinoga
Keyword(s):  
Heavy Rainfall ◽  
Temporal Distribution ◽  
Heavy Rain ◽  
Rain Gauge ◽  
Weather Types ◽  
Seasonal Incidence ◽  
Rainfall Events ◽  
Synoptic Conditions ◽  
The Mediterranean ◽  
Heavy Rainfall Events

Heavy rainfall events in the Mediterranean can be of high intensity, commonly exceeding 100 mm day-1, and have irregular spatio-temporal distribution. Such events can have significant impacts both on soils and human structures. The aim of this paper is to highlight a systematic comparison of synoptic conditions with heavy rainfall events in Mediterranean Southern Spain, assessing the weather types responsible for meteorological risk in specific locations of this mountainous region. To do this, we analyzed the maximum intensity of rainfall in observational periods ranging from 10 min to 24 h using a database from 132 rain gauge stations across the study area since 1943; then, the heavy rain has been associated with the weather type which triggers it. This analysis identified a pattern of heavy rainfall which differs from that previously reported in the Mediterranean area. Thus, in this research, the maximum number of heavy rainfall events uses to come from a dominant pattern of low pressures associated to front systems and East-Northeast winds; but the maximum volumes use to be associated to Cold Drops and the same winds; in addition, there are differences throughout the territory, showing several patterns and seasonal incidence when analyzing sub-zones, which may be related with different erosive conditions according to its position with respect to Atlantic or Mediterranean sea, and the entity of its relief.

scholarly journals Conditional Generative Adversarial Networks (cGANs) for Near Real-Time Precipitation Estimation from Multispectral GOES-16 Satellite Imageries—PERSIANN-cGAN

2019 ◽  
Vol 11 (19) ◽  
pp. 2193 ◽  
Author(s):  
Negin Hayatbini ◽  
Bailey Kong ◽  
Kuo-lin Hsu ◽  
Phu Nguyen ◽  
Soroosh Sorooshian ◽  
...  
Keyword(s):  
Mean Squared Error ◽  
Probability Of Detection ◽  
Generative Adversarial Networks ◽  
Generative Adversarial Network ◽  
Convolutional Network ◽  
Critical Success ◽  
Adversarial Network ◽  
Precipitation Estimation ◽  
Precipitation Estimates ◽  
Global Precipitation

In this paper, we present a state-of-the-art precipitation estimation framework which leverages advances in satellite remote sensing as well as Deep Learning (DL). The framework takes advantage of the improvements in spatial, spectral and temporal resolutions of the Advanced Baseline Imager (ABI) onboard the GOES-16 platform along with elevation information to improve the precipitation estimates. The procedure begins by first deriving a Rain/No Rain (R/NR) binary mask through classification of the pixels and then applying regression to estimate the amount of rainfall for rainy pixels. A Fully Convolutional Network is used as a regressor to predict precipitation estimates. The network is trained using the non-saturating conditional Generative Adversarial Network (cGAN) and Mean Squared Error (MSE) loss terms to generate results that better learn the complex distribution of precipitation in the observed data. Common verification metrics such as Probability Of Detection (POD), False Alarm Ratio (FAR), Critical Success Index (CSI), Bias, Correlation and MSE are used to evaluate the accuracy of both R/NR classification and real-valued precipitation estimates. Statistics and visualizations of the evaluation measures show improvements in the precipitation retrieval accuracy in the proposed framework compared to the baseline models trained using conventional MSE loss terms. This framework is proposed as an augmentation for PERSIANN-CCS (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Network- Cloud Classification System) algorithm for estimating global precipitation.

Verification of Tropical Cyclone–Related Satellite Precipitation Estimates in Mainland China

2009 ◽  
Vol 48 (11) ◽  
pp. 2227-2241 ◽  
Author(s):  
Zifeng Yu ◽  
Hui Yu ◽  
Peiyan Chen ◽  
Chuanhai Qian ◽  
Caijun Yue
Keyword(s):  
Heavy Rainfall ◽  
Rain Rate ◽  
Mainland China ◽  
Tropical Rainfall Measuring Mission ◽  
Rain Gauge ◽  
Horizontal Distribution ◽  
Rainfall Events ◽  
Light Rain ◽  
Geostationary Meteorological Satellite ◽  
Heavy Rainfall Events

Abstract To evaluate the abilities of satellite retrievals in reflecting precipitation features related to tropical cyclones (TCs) affecting mainland China, four years of 6- and 24-h precipitation retrievals from three datasets, namely the Tropical Rainfall Measuring Mission satellite algorithm 3B42, version 6 (3B42), Climate Prediction Center morphed (CMORPH) product, and one based on the Geostationary Meteorological Satellite-5 infrared brightness temperature (GMS5-TBB), are compared statistically with direct measurements from surface gauge rainfall data during the periods affected by TCs. The GMS5-TBB dataset was set up by a method of considering the GMS5-TBB characteristics, hourly precipitation intensity, and horizontal distribution for landfalling TCs. The results show that in a general sense, all three satellite-retrieved rainfall datasets give quite reasonable 6- and 24-h rainfall distributions, with skill decreasing with the increase in both latitude and rainfall amount. The 3B42 has a little bit better skill than CMORPH, which is likely related to the fact that the 3B42 product has a rain gauge adjustment and CMORPH does not. Further analyses show that both 3B42 and CMORPH considerably underestimate the moderate and heavy rainfall and overestimate the very light precipitation. The overestimation of the GMS5-TBB data for the light rain is larger than that for 3B42 and CMORPH, probably due to the fact that the GMS5-TBB method considers stratiform and convective rainfall separately with a fixed stratiform rain rate of 2 mm h−1. For the heavy rainfall events, the GMS5-TBB data perform much better than the 3B42 and CMORPH data with an almost halved bias, owing to the fact that the GMS5-TBB method adopted the adjustment of the convective rain rate by considering TBB characteristics of landfalling TCs and using hourly gauge rainfall in the setup process. Since the heavy rainfall events associated with landfalling TCs are of the most concern, the compared GMS5-TBB data could be useful as an operational/research reference.

scholarly journals Evaluation of Indian summer monsoon rainfall features using TRMM and KALPANA-1 satellite derived precipitation and rain gauge observation

MAUSAM ◽  
2021 ◽  
Vol 61 (3) ◽  
pp. 317-336
Author(s):  
V. R. DURAI ◽  
S. K. ROY BHOWMIK ◽  
B. MUKHOPADHYAY
Keyword(s):  
Summer Monsoon ◽  
Western Ghats ◽  
Heavy Rainfall ◽  
Heavy Rain ◽  
Rain Gauge ◽  
Summer Monsoon Rainfall ◽  
North East ◽  
The North ◽  
Western Ghats Of India ◽  
The Western Ghats

The study provides a concise and synthesized documentation of the current level of skill of the satellite (3B42RT, 3B42V-6, KALPANA-1) products over Indian regions based on the data gathered during the summer monsoon seasons of 2006, 2007 and 2008. The inter-comparison of satellite products with the rain gauge observations suggests that the TRMM 3B42V6 product could distinctly capture characteristic features of the summer monsoon, such as north–south oriented belt of heavy rainfall along the Western Ghats with sharp gradient of rainfall between the west coast heavy rain region and the rain shadow region to the east, pockets of heavy rainfall along the location of monsoon trough, over the east central parts of the country, over north-east India, along the foothills of Himalayas and over the north Bay of Bengal. The KALPANA-1 and 3B42RT products reproduce only the broadest features of mean monsoon seasonal rainfall. The near real-time products 3B42RT and KALPANA-1 underestimate the orographic heavy rainfall along the Western Ghats of India. The precipitation estimates from TRMM 3B42V6 product, when accumulated over the whole season, could reproduce the observed pattern. However, the TRMM 3B42RT and KALPANA-1 estimates are comparatively lower than the observed rainfall over most parts of the country during the season. Inter comparison reveals that the TRMM 3B42V6 product showed better skill in estimating the daily and seasonal mean rainfall over all India and also over four homogeneous regions of India.  

Calibration of TRMM Derived Rainfall Over Nepal During 1998-2007

2010 ◽  
Vol 4 (1) ◽  
pp. 12-23 ◽  
Author(s):  
Md. Nazrul Islam ◽  
Someshwar Das ◽  
Hiroshi Uyeda
Keyword(s):  
Daily Rainfall ◽  
Tropical Rainfall Measuring Mission ◽  
Calibration Procedure ◽  
Skill Score ◽  
Rain Gauge ◽  
Probability Of Detection ◽  
Similar Distribution ◽  
Rain Gauge Data ◽  
Gauge Data ◽  
Adjustment Factors

In this study rainfall is calculated from Tropical Rainfall Measuring Mission (TRMM) Version 6 (V6) 3B42 datasets and calibrated with reference to the observed daily rainfall by rain-gauge collected at 15 locations over Nepal during 1998-2007. In monthly, seasonal and annual scales TRMM estimated rainfalls follow the similar distribution of historical patterns obtained from the rain-gauge data. Rainfall is large in the Southern parts of the country, especially in the Central Nepal. Day-to-day rainfall comparison shows that TRMM derived trend is very similar to the observed data but TRMM usually underestimates rainfall on many days with some exceptions of overestimation on some days. The correlation coefficient of rainfalls between TRMM and rain-gauge data is obtained about 0.71. TRMM can measure about 65.39% of surface rainfall in Nepal. After using calibration factors obtained through regression expression the TRMM estimated rainfall over Nepal becomes about 99.91% of observed data. TRMM detection of rainy days is poor over Nepal; it can approximately detect, under-detect and over-detect by 19%, 72% and 9% of stations respectively. False alarm rate, probability of detection, threat score and skill score are calculated as 0.30, 0.68, 0.53 and 0.55 respectively. Finally, TRMM data can be utilized in measuring mountainous rainfall over Nepal but exact amount of rainfall has to be calculated with the help of adjustment factors obtained through calibration procedure. This preliminary work is the preparation of utilization of Global Precipitation Measurement (GPM) data to be commencing in 2013.

scholarly journals Thailand Daily Rainfall and Comparison with TRMM Products

2008 ◽  
Vol 9 (2) ◽  
pp. 256-266 ◽  
Author(s):  
Roongroj Chokngamwong ◽  
Long S. Chiu
Keyword(s):  
Daily Rainfall ◽  
Tropical Rainfall Measuring Mission ◽  
Heavy Rain ◽  
Absolute Difference ◽  
Rain Event ◽  
Critical Success ◽  
Global Precipitation Climatology Centre ◽  
Temporal And Spatial ◽  
Rain Events ◽  
Rain Rates

Abstract Daily rainfall data collected from more than 100 gauges over Thailand for the period 1993–2002 are used to study the climatology and spatial and temporal characteristics of Thailand rainfall variations. Comparison of the Thailand gauge (TG) data binned at 1° × 1° with the Global Precipitation Climatology Centre (GPCC) monitoring product shows a small bias (1.11%), and the differences can be reconciled in terms of the increased number of stations in the TG dataset. Comparison of daily TG with Tropical Rainfall Measuring Mission (TRMM) version 6 (V6) 3B42 rain estimates shows improvements over version 5 (V5) in terms of bias and mean absolute difference (MAD). The V5 is computed from the adjusted Geostationary Operational Environmental Satellite (GOES) precipitation index (AGPI) and V6 is computed using the TRMM Multisatellite Precipitation Analysis (TMPA) algorithm. The V6 histogram is much closer to that of TG than V5 in terms of rain fraction and conditional rain rates. Scatterplots show that both versions of the satellite products are deficient in capturing heavy rain events. In terms of detecting rain events, a critical success index (CSI) shows no difference between V6 and V5 3B42. The CSI for V6 is higher for the rainy season than the dry season. These results are generally insensitive to rain-rate threshold and averaging periods. The temporal and spatial autocorrelation of daily rain rates for TG, V6, and V5 3B42 are computed. Autocorrelation function analyses show improved temporal and spatial autocorrelations for V6 compared to TG over V5 with e-folding times of 1, 1, and 2 days, and isotropic spatial decorrelation distances of 1.14°, 1.87°, and 3.61° for TG, V6, and V5, respectively. Rain event statistics show that the V6 3B42 overestimates the rain event durations and underestimates the rain event separations and the event conditional rain rates when compared to TG. This study points to the need to further improve the 3B42 algorithm to lower the false detection rate and improve the estimation of heavy rainfall events.

scholarly journals Evaluation of CMPA precipitation estimate in the evolution of typhoon-related storm rainfall in Guangdong, China

2016 ◽  
Vol 18 (6) ◽  
pp. 1055-1068 ◽  
Author(s):  
Dashan Wang ◽  
Xianwei Wang ◽  
Lin Liu ◽  
Dagang Wang ◽  
Huabing Huang ◽  
...  
Keyword(s):  
Correlation Coefficients ◽  
Extreme Rainfall ◽  
Heavy Rain ◽  
Rain Gauge ◽  
Probability Of Detection ◽  
Spatiotemporal Evolution ◽  
Light Rain ◽  
Storm Rainfall ◽  
Climate Prediction Center ◽  
Rain Gauge Network

The merged precipitation data of Climate Prediction Center Morphing Technique and gauge observations (CMPA) generated for continental China has relatively high spatial and temporal resolution (hourly and 0.1°), while few studies have applied it to investigate the typhoon-related extreme rainfall. This study evaluates the CMPA estimate in quantifying the typhoon-related extreme rainfall using a dense rain gauge network in Guangdong Province, China. The results show that the event-total precipitation from CMPA is generally in agreement with gauges by relative bias (RB) of 2.62, 10.74 and 0.63% and correlation coefficients (CCs) of 0.76, 0.86 and 0.91 for typhoon Utor, Usagi and Linfa events, respectively. At the hourly scale, CMPA underestimates the occurrence of light rain (&lt;1 mm/h) and heavy rain (&gt;16 mm/h), while overestimates the occurrence of moderate rain. CMPA shows high probability of detection (POD = 0.93), relatively large false alarm ratio (FAR = 0.22) and small missing ratio (0.07). CMPA captures the spatial patterns of typhoon-related rain depth, and is in agreement with the spatiotemporal evolution of hourly gauge observations by CC from 0.93 to 0.99. In addition, cautiousness should be taken when applying it in hydrologic modeling for flooding forecasting since CMPA underestimates heavy rain (&gt;16 mm/h).

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