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Accurate estimation of precipitation is crucial for yield assessment, flood and drought monitoring and water resources management. Rainfall consists of both temporal and spatial variability. Rain gauges support temporal resolution, on the other hand it is weakness in the quality of spatial resolution. However, remote sensing provides good spatial r...
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... IMD This product: section a focus low negative on TRMM bias (-0.96 3B43 In daily comparison section, zonal mm/day) monthly products, compare 3B42 to TRMM daily products, 3B42 product CPC operation Figure 7 was show applied the to high analyze underestimates the rainfall (-1.24 and IMD mm/day) products and show analyzed lower MAD by zonal (1.41 in products high rainfall of TRMM areas from 3B42 figure 3 and CPC are shown with vs. operation 1.62) and over higher states accuracy and cut (76.04 some states vs. 69.11). that in IMD Pondicherry, in each state. Karnataka, Indian Andhra Meteorological Pradesh, The very CPC small (mm/day) : Chandigarh, is consistently Dadra lower & Nagar than Jammu and Kashmir, Himachal Pradesh, the Haveli, TRMM Delhi, 3B42 Goa estimates and Pondicherry. (-84.24 vs. -127.78 Meghalayaand Arunachal Pradesh that have mm/day). higher rainfall than other regions. The overestimates areas are found in middle-north states of India such as Madhya Pradesh, Rajathan and Orissa. to IMD This product: section a focus low negative on TRMM bias (-0.96 3B43 In daily comparison section, zonal mm/day) monthly products, compare 3B42 to TRMM daily products, 3B42 product CPC operation Figure 7 was show applied the to high analyze underestimates the rainfall (-1.24 and IMD mm/day) products and show analyzed lower MAD by zonal (1.41 in products high rainfall of TRMM areas from 3B42 figure 3 and CPC are shown with vs. operation 1.62) and over higher states accuracy and cut (76.04 some states vs. 69.11). that in IMD Pondicherry, in each state. Karnataka, Indian Andhra Meteorological Pradesh, The very CPC small (mm/day) : Chandigarh, is consistently Dadra lower & Nagar than Jammu and Kashmir, Himachal Pradesh, the Haveli, TRMM Delhi, 3B42 Goa estimates and Pondicherry. (-84.24 vs. -127.78 Meghalayaand Arunachal Pradesh that have mm/day). higher rainfall than other regions. The overestimates areas are found in middle-north states of India such as Madhya Pradesh, Rajathan and Orissa. This section focus on TRMM 3B43 monthly products, 3B42 daily products, CPC and IMD products analyzed by zonal operation over states and cut some states that very small : Chandigarh, Dadra & Nagar Haveli, Delhi, Goa and Pondicherry. In daily comparison section, zonal operation was applied to analyze the rainfall products of TRMM 3B42 and CPC with IMD in each state. Indian Meteorological Department rainfall over states in monsoon of monthly The comparison comparison of TRMM result. 3B43 monthly season is shown in figure 8. High rainfall products, 3B42 3-hourly products and the regions are Northern, Eastern and South- Climate Prediction Center (CPC) with India western India that shown in figure 9. The states Meteorological Department (IMD) gauges, that situated in high rainfall regions tend to TRMM 3B42 and 3B43 products have higher show higher bias that shown in figure 10. From figure 11, the underestimates and overestimates areas are found in the same states The comparison of TRMM 3B43 monthly products, 3B42 3-hourly products and the Climate Prediction Center (CPC) with India Meteorological Department (IMD) gauges, TRMM 3B42 and 3B43 products have ...
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... Remote sensing, such as weather radar and meteorological satellites, has dramatically been improved at present. It can be widely applied to various missions, for example, science, environmental engineering, natural disaster and other related fields [1][2][3]. One important mission on meteorology includes measurement of rainfall amount to be used for analyzing and monitoring, as well as natural disaster warnings and weather forecast. ...
This research aimed to study the rainfall estimation with weather radar in southern Thailand. Both precipitation data from Sathing-Phra radar station, Songkhla, and hourly rainfall data from automatic tipping bucket rain gauges are of the Thai Meteorological Department (TMD) in 2012-2013. These data were used to study the relationship between radar reflectivity (Z) and intensity of the rainfall (R) in both monsoon season. The rain-rate obtained from radar with Z-R equation was a precise rain estimate method. The calculation provided the rain-rate more accurate than original equation often used. The equation of this study was best used during the northeast monsoon which occurred in rainy season in eastern coast of the South (Thailand's Gulf). However, the result of this study should be improved to estimate rain-rate by radar for southern Thailand and, as input data, applied for applications of early warning systems.
... The lower part has extensive irrigation networks and hence intensive rice paddy cultivation. The study about estimation and prediction of rainfall are absolutely necessary for an effective water management in agriculture area [12][13][14][15][16][17].The geography of central region is shown in Figure 2. ...
Weather radar is an important tool that integrates meteorology and hydrology. It plays an important part in the hydrology and it is also associated with the quantitative use of radar measurements that find the relationship between radar reflectivity (Z) and rain intensity (R) such as Z-R relationships. To obtain better rainfall estimation for daily rainmaking purposes over the central region of Thailand, the calibration of the Z-R relationship were estimated using the series of Z-R pair data which derived from weather radar and rain gauge network of DRRAA and TMD. The a and b parameters of Z-R relationship will be calibrated by minimizing the Root Mean Square Error (RMSE) and linear regression method between radar and corresponding rain gauge rainfall with the constant of b parameter of 1.5. The 98 convective rainfall events during rainy season in May to September 2013 over the middle part of Thailand are chosen then these selected rainfall events were divided into two groups. The first group of 35 events with rain intensity from DRRAA rain gauges was used to Z-R calibration and derived parameters. The second group of 63 events with rainfalls from MET were considered as validated data. The Z-R relationships were derived using RMSE as Z = 144R^1.5 and a regression method as Z = 206R^1.5. These rainfall events were used to test an accuracy of the proposed radar rainfall estimation based on different Z-R relationship including Z-R relationships obtained from the calibration. It was evident that using of proposed Z-R relationship in rainy season over central Thailand; Z=144R^1.5, gave the smallest RMSE compared to using Z=206R^1.5, Z = 200R^1.6 and Z = 300R^1.4.
... ปั จจุ บั นเทคโนโลยี การรั บรู ้ จากระยะไกล (Remote sensing) เช่ น เรดาร์ ตรวจอากาศ และดาวเที ยมอุ ตุ นิ ยมวิ ทยา มี การพั ฒนาขึ ้ นอย่ างรวดเร็ ว และสามารถประยุ กต์ ใช้ ส าหรั บภารกิ จด้ านต่ างๆได้ อย่ างกว้ างขวาง เช่ น วิ ทยาศาสตร์ วิ ศวกรรมศาสตร์ สิ ่ งแวดล้ อม และสาขาอื ่ นๆ ที ่ เกี ่ ยวข้ อง [1- 3]. ส าหรั บภารกิ จด้ านอุ ตุ นิ ยมวิ ทยาที ่ ส าคั ญประการหนึ ่ งได้ แก่ การตรวจวั ด ปริ มาณฝนเพื ่ อใช้ ในการวิ เคราะห์ เพื ่ อการติ ดตามสถานการณ์ เตื อนภั ย ธรรมชาติ และพยากรณ์ อากาศ ได้ มี การประยุ กต์ ใช้ เทคโนโลยี เรดาร์ ตรวจ อากาศ [4] [5] [6] และดาวเที ยมอุ ตุ นิ ยมวิ ทยา [7] [8] มาใช้ ส าหรั บภารกิ จด้ าน การประมาณปริ มาณฝนเพื ่ อลดข้ อจ ากั ดของการตรวจวั ดปริ มาณฝนด้ วย วิ ธี การปกติ ที ่ ข้ อมู ลที ่ ได้ จะมี รู ปแบบเชิ งจุ ด และไม่ ครอบคลุ มพื ้ นที ่ ห่ างไกล ป่ ารกทึ บ ภู เขา บึ งน้ าขนาดใหญ่ และทะเล [9] [10] [11] จึ งท าให้ ประสิ ทธิ ภาพใน การเตื อนภั ยและการพยากรณ์ อากาศเพิ ่ มขึ ้ นกว่ าในอดี ต จากลั กษณะของภู มิ ประเทศบริ เวณชายฝั ่ งทะเลภาคใต้ ของประเทศไทย ที ่ ถู กขนาบด้ วยทะเลทั ้ งสองฝั ่ งและมี แนวเทื อกเขาขั ้ นกลางขนานกั บแนว ทะเล เป็ นสาเหตุ ให้ ฝนตกหนั กและมี ปริ มาณฝนสะสมมากบริ เวณด้ านรั บลม ( ...
This research aims to study relationship between reflectivity of radar and rainfall intensity in monsoon season for verification performance of radar based rainfall estimation in southeastern region of Thailand. The reflectivity data of Sating Phra station at Songkla province together with hourly rainfall between 2012 and 2013 were used in this experiment. The study finds that relationship between reflectivity data and intensity of rainfall have capacity to estimate hourly rainfall in remote area which has no rain gauge station. The information of rainfall pattern and trajectory of rainfall also has better performance than original version of rainfall estimation. To evaluate performance of radar based rainfall estimation application, the product of this application were compared with radar based rainfall in earlier study by statistical method. The developed application performs better than earlier application. It appropriates Thai Meteorology Department’s operations to be adopted to use.
... Many researchers have been trying to estimate the amount of precipitation to be used in various activities regarding the research that defined its use [4][5][6][7][8][9]. As the precipitation is the key factor that caused flooding; therefore, the problem we faced in the research is the insufficiency of input data with spatial resolution [10][11][12]. Figures 2 and 3 explain the real operation of the previous development of the heavy rainfall warning system by automatic rain gauges and Geostationary Satellite [13] . ( Source: http:// www.apectyphoon.org/sdt175/img/img/3870/PDF/Day2/Session5. ...
The south of Thailand is the wettest region of the country. It has two sides of the peninsula: east and west coast. The two sub regions have different period of wet season. In eastern South coast, the rains lasts from November to February while the western South, Andaman Sea coast, is forecast outbreaks of showers in May to October. This nature, along with massive water in the catchments, can trigger flash floods, overbank flows, and landslides down each sub region if torrential rain occurs. Satellite-based rainfall, an indirect measure with limitation though, plays a key role in approaching data that cover remote and terrain areas. With this technique, if the rainfall data are proved true, it benefits the notice of possible downpour in the case study. In the validation process, the Global Satellite Mapping of Precipitation (GSMaP) product and rainfall data of the Thai Meteorological Department (TMD) from 2010-2011 are used to correct equation for the monsoon and
the off-monsoon period. The data from 2012-2013 are validated for TMD’s point rain gauges. Likewise, the root mean square error (RMSE) and the mean absolute error (MAE) are applied. The bias correcting satellite-based rainfall estimation provides better effective primary data set regarding spatial and temporal coverage. In progress, the probability of detection (POD) and the false alarm ratio (FAR) are applied to achieve heavy rainfall warning system. As a result, POD’s score is extremely acceptable. Therefore, the alerting system, without point rain gauge, can be applied for heavy rainfall in isolated area.
... Although recently there is ground radar technology for measuring the rainfall, the device is limited to certain extend of kilometres only and uncover areas monitored by other station which creates inconsistency. With the innovation of highly spatiotemporal , radar, and remote sensing, it provides a solution on limitation of previous generation technology (Chanyatham and Kirtsaeng, 2011). In the past few years, several attempts to obtain the rainfall were done by launching numerous satellites orbiting the earth (Turk and Miller, 2005). ...
Rainfall, which is sub-precipitation that occurs highly in tropical region, plays a vital role for earth energy balance and living creature. With the advance of satellite, rainfall over large area coverage can be measured in a better spatial resolution. In every released of data Tropical Rainfall Measuring Mission (TRMM) such as 3B43 product series, an improvement from the previous version is very substantial, particularly in better algorithm. This article presents the validation of latest TRMM satellite-based 3B43 v7 product for localizing error in Peninsular Malaysia. The validation time range covers from 2000-2010 were evaluated with a total of 26 sets of rain gauge observation recorded by Malaysia Meteorological Department (MMD). The main objective of this study is to validate the TRMM 3B43-v7 product against corresponding in-situ measured rainfall. It is found that version 7 is improved in terms of spatial distribution (61.53% stations reduction) when analysed using Mean Absolute Error (MAE), lowering maximum bias (111.87mm to 81.67). Despite the changes occurrences, the value is at maximum Δ0.2 in correlation aspect. However, in Nash-sutcliffe (Nr) correlation, 53.84% stations were under estimate and perform low. Only 46.15% stations show good agreement for hydrological purpose, which is still reasonable. It is recommended that the TRMM data calibrated for operational hydrometeorology applications.
... The numbers of rain gauge are limited with less equipped in remote area and rainfall is high spatial and temporal variability. Therefore, rainfall data obtained by remote sensing technique is been widely used now because this technique was successfully in overcoming the major shortcoming of rain gauge measurement (Yilmaz et al., 2005;Chanyatham and Kirtsaeng, 2011). ...
Water is one of the most important of natural resource for all living things. In certain areas, water tends to be very limited and there are situation where th e availability of water is always deficient. This phenomena will altered by the effect of global warming. The main source of water is rainfall. Understanding of rainfall characteristics is very essential. The main objective of this study is to evaluate the reliability of Tropical Rainfall Measuring Mission (TRMM) 3B43 algorithm to study Peninsular Malaysia's rainfall distribution. Ten years of TRMM 3B43 data from 1998 to 2007 were validated by 24 rain gauge measurements. Four statistical parameters were used to determine the reliability of TRMM data. Results of this study show that TRMM data is overestimated and low accuracy. It might be due to the echoes error of Precipitation Radar (PR) on TRMM satellite. The TRMM data is still be considered reliable to analysis the annual Peninsular Malaysia rainfall pattern. Further research should be proceeded to analyze in-depth the results and enhance the reliability of TRMM data.
... The rainfall products were compared with the TRMM observation. Its data included microwave precipitation, microwave-calibrated IR precipitation, microwave overpass time and gauge station [18]. Furthermore, the outputs were also compared to the data from TMD rain gauge station. ...
In seasonal northeast monsoon, heavy rainfalls caused many natural disasters in the southern east-coast of Thailand. A thorough study of simulations of heavy precipitation phenomena could provide a better understanding of their nature, inducing the better forecasting of similar cases. The cumulus parameterization technique was used to determine the appropriate cumulus physics for a model. Three cumulus parameterization schemes, such as, the Kain-Frisch (KF), the Grell-Devenyi (GD), and the Betts-Miller-Janjic (BMJ) schemes were applied for the Weather Research and Forecasting (WRF–ARW version) modeling system. Their performances were evaluated by validation of the simulated rainfall by mean of calculating the categorical skill scores, such as, frequency bias (BIAS), critical success index (CSI), and equitable threat scores (ETS). The rainfall data observed from the Tropical Rainfall Measuring Mission (TRMM), and the Thai Metrological Department (TMD) were used to verify the rainfall simulation results. During the heavy rainfall events in this study, the maximum rainfalls simulated by KF, GD and BMJ schemes were recorded 146, 120, and 38 mm, respectively, while TRMM showed 187 mm of the maximum rainfall. The specific location of the intense rainfalls and their magnitude were excellently simulated in KF scheme. The positions of the maximum rainfall simulated by KF and GD schemes were also more accurate than the positions simulated by BMJ scheme. The categorical skill scores evaluation revealed that the KF scheme outperforms the GD and BMJ schemes if heavy precipitation is used as the threshold.
Estimation of precipitation data is crucial due to high dependency of water resources management, crop yield assessment, flood and drought monitoring on precipitation data. Devastating crop yields which limit human consumption due to drought are the serious consequences of extended absence of rainfall. On the other hand excessive rainfall causes flooding and severe damage to property and lives. Bangladesh is also heavily dependent on rain-fed agriculture, hence excess or insufficient rainfall can be devastating to the national economy and the livelihood of its people. Flash flood occurs in almost every year during pre-monsoon to post-monsoon months (March-May) in North East region of Bangladesh. But ground based rainfall data available for the North East region is not so consistent and reliable always due to excessive missing values and errors in the collected data. This could be a limitation in the simulation of flood water levels for this region. Therefore, possibility of improving the understanding of rainfall distribution by incorporating the rainfall estimates from a spatially distributed, comprehensive and systematic source like the Tropical Rainfall Measuring Mission (TRMM) is explored in this paper. TRMM is specially designed to increase the extent and accuracy of tropical rainfall measurement. In this paper, eight years of data from 1999 to 2006 of TRMM 3B42 version 6 (V6), which allows aggregating the values to a daily, monthly and yearly resolution and the Bangladesh Water Development Board (BWDB) rain gauge network are analysed to understand the rainfall characteristics over North-East region of Bangladesh. Daily rainfall measured by TRMM 3B42 version 6 is compared to that of rain gauge values from pre-monsoon to post-monsoon months (March–May), which results following statistical measures: BIAS, MAD and RMSD. Finally, a co-relation is established between remote sensing based TRMM data and ground based precipitation data for the North-East region of Bangladesh by analysing those statistical parameters. Results of this study will be helpful to update and complete the existing rainfall database, simulate the flash flood in time and enrich more rainfall relevant research studies in future for the North-East region of Bangladesh.
The Numerical Weather Prediction (NWP) plays a vital role in short-range rainfall forecasting. The atmospheric physical parameters have been continuously developed for the NWP. These parameters are implemented into NWP models based on equations derived from theoretical assumptions based on observations and physics of the event. The aim of study is to investigate the use of three cumulus parameterization schemes (CPS), namely, Kain-Fritsch (KF), Bett-Miller-Janjic (BMJ) and Grell-Devenyi (GD), in the version 3.5.2 of Weather Research and Forecasting (WRF) model for the off-season heavy rainfall caused by cold surge during the pre-monsoon (April) of 2013 in the east coast of southern Thailand. The initial conditions preceding the heavy rainfall events, 24, 48, and 72 forecasts were simulated using the WRF with three different schemes. The performance of the schemes was measured by the comparison with observed rainfall data from Thai Meteorological Department and Global Satellite Mapping of Precipitation (GSMaP). The result shows that all of schemes can detect the rainfall amount with the forecast from the GD being higher than that of the KF and BMJ scheme. The statistical indicators utilized to compare the performance of the three schemes include critical success index (CSI), equitable threat score (ETS). Two statistical indicators support the finding that the BMJ scheme has a slightly better performance than KF and BMJ, except Probability of detection (POD) that KF has the best skill when used in off-season rainfall forecasts over the east coast of southern Thailand.
INSAT-3D is the new generation Indian Earth observation satellite to be launched by ISRO in near future. It will provide enhanced observations of land, ocean and atmosphere through two payloads namely Imager and Sounder from geostationary orbit. The Imager payload is designed to sense solar-reflected and radiant energy from the Earth through six channels covering visible (VIS: 0.55-0.75µ m), shortwave infrared (SWIR: 1.551.70µm), middle infrared (MIR: 3.8-4.0µm), water vapor infrared (WVIR: 6.5-7.1µm) and thermal infrared (TIR1: 10.3-11.3µm and TIR2: 11.5-12.5µm) spectral regions. The TIR1 and TIR2 are two thermal channels that can be used to derive a key geophysical parameter- land surface temperature (LST) through a split-window algorithm. To derive LST from various satellite-based sensors several types of split-window (SW) algorithms have been developed in the past 25 years; however, SW algorithm for Indian sensor has not been attempted. A study has been conducted with the objective to identify the most suitable SW algorithm for LST retrieval from the INSAT-3D Imager thermal infrared observations. A systematic exercise was carried out involving evaluation and comparison of 10 published SW algorithms. The evaluation of algorithms was done through two major steps: (a) Simulation of at-sensor thermal signal by a radiative transfer model- MODTRAN (MODerate spectral resolution atmospheric TRANSmittance) and (b) Derivation of split-window coefficients (SWC) of 10 algorithms of LST retrieval. The simulations were carried out in the split thermal channels by varying following input parameters: temperature of the atmospheric lower boundary through atmospheric profiles, atmospheric columnar water vapour, surface temperature, view zenith angle and land surface emissivity over 10 different land cover types. Once the simulation process was over, the SWC were generated for 10 LST algorithms. The results were inter-compared by computing the total error in the estimation of LST by classical error theory. Based upon the inter comparison analysis, we found that Sobrino et al. (1996) performed superior with coefficient of determination (R) of more than 0.96 and the RMSE of around 1.43 K therefore it is selected for further analysis.
