Quantitative Precipitation Estimation (QPE) Rainfall from Meteorology Radar over Chi Basin

Abstract

This study of the Quantitative Estimation Precipitation (QEP) of rainfall, detected by two Meteorology Radars over Chi Basin, North-east Thailand, used data from the Thai Meteorological Department (TMD). The rainfall data from 129 rain gauge stations in the Chi Basin area, covering a period of two years, was also used. The study methodology consists of: firstly, deriving the QPE between radar and rainfall based on meteorological observations using the Marshall Palmer Stratiform, the Summer Deep Convection, and Regression Model and calibrating with rain gauge station data; secondly, Bias Correction using statistical method; thirdly, determining spatial variation using three methods, namely Kriging, Inverse Distance Weight (IDW), and the Minimum Curvature Method. The results of the study demonstrated the accuracy of estimating precipitation using meteorological radar. Estimated precipitation compared against an equivalent of 2 years of rain station measurement had a probability of detection (POD) of 0.927, where a value of 1 indicated perfect agreement, demonstrating the effectiveness of the method used to calibrate the radar data. The bias correction method gave high accuracy compared with measured rainfall. Furthermore, of the spatial estimation of rainfall methods, the Kriging methodology showed the best fit between estimation of rainfall distribution and measured rainfall distribution. Therefore, the results of this study showed that the rainfall estimation, using data from a meteorology radar, has good accuracy and can be useful, especially in areas where it is not possible to install and operate rainfall measurement stations, such as in heavily forested areas and/or in steep terrain. Additionally, good accuracy rainfall data derived from radar data can be integrated with other data used for water management and natural disasters for applications to reduce economic losses, as well as losses of life and property.

Comparing Rain Gauge and Weather Radar Data in the Estimation of the Pluviometric Inflow from the Apennine Ridge to the Adriatic Coast (Abruzzo Region - Central Italy)

“…These diagrams confirm the higher values in gauging stations' datasets as already seen in Figure 5 and more importantly, they come up with a way to correct radar estimates in complex orography or wide areas [20]. Indeed, it seems that the differences (bias) that characterize the two curves (dotted blue and green one) in Figure 5 are almost parallel to each other thus suggesting that the bias found at lower altitude is applicable to higher ones.…”

“…Despite all the above-mentioned critical factors, weather radar-derived rainfall datasets are valuable with respect to rain gauge networks because they can potentially reduce the uncertainty about precipitation inflow volumes thanks to their higher spatialtemporal resolution. Weather radars are nowadays networked and generally used for weather surveillance, hydrological and meteorological purposes with special emphasis on data assimilation [17], in support to civil protection activities [18] as well as to better define the water budget in regional aquifers [19] or in wide catchment [20].…”

Comparing Rain Gauge and Weather RaDAR Data in the Estimation of the Pluviometric Inflow from the Apennine Ridge to the Adriatic Coast (Abruzzo Region, Central Italy)

“…These diagrams confirmed the higher values in the gauge station datasets as already seen in Figure 5; more importantly, they come up with a way to correct radar estimates in complex orography or wide areas [21]. Indeed, the differences (bias) that characterize the two curves (dotted blue and green curves) in Figure 6, which are almost parallel to each other, seemingly suggest that the bias found at a lower altitude is applicable to that at a higher altitude.…”

“…Despite all the above-mentioned critical factors, weather-radar-derived rainfall datasets are valuable with respect to rain gauge networks because they can potentially reduce the uncertainty about precipitation inflow volumes due to their higher spatial-temporal resolution. Weather radars are currently networked and generally used for weather surveillance, hydrological, and meteorological purposes with a special emphasis on data assimilation [18], in support of civil protection activities [19], as well as to better define the water budget in regional aquifers [20] or in wide catchments [21].…”

Estimating a Reliable Water Budget at a Basin Scale: A Comparison between the Geostatistical and Traditional Methods (Foro River Basin, Central Italy)

“…Also, the traditional approach could benefit from a higher number of point data. The recent developments obtained with the use of weather RaDAR data are encouraging [9,39,40] and may represent a valuable additional source of information to be integrated into the water budget estimation, especially through an advanced geostatistical approach (e.g, Multi-Collocated Co-Kriging or Kriging with External Drift). The use of weather RaDAR data would allow estimating in a more reliable way the spatial distribution of rainfall on a finer grid mesh and with a lower associated uncertainty.…”