Evaluation of high-resolution satellite precipitation products with surface rain gauge observations from Laohahe Basin in northern China

Three high-resolution satellite precipitation products, the Tropical Rainfall Measuring Mission （TRMM） standard precipitation products 3B42V6 and 3B42RT and the Climate Precipitation Center＇s （CPC） morphing technique precipitation product （CMORPH）, were evaluated against surface rain gauge observations from the Laohahe Basin in northern China. Widely used statistical validation indices and categorical statistics were adopted. The evaluations were performed at multiple time scales, ranging from daily to yearly, for the years from 2003 to 2008. The results show that all three satellite precipitation products perform very well in detecting the occurrence of precipitation events, but there are some different biases in the amount of precipitation. 3B42V6, which has a bias of 21%, fits best with the surface rain gauge observations at both daily and monthly scales, while the biases of 3B42RT and CMORPH, with values of 81% and 67%, respectively, are much higher than a normal receivable threshold. The quality of the satellite precipitation products also shows monthly and yearly variation： 3B42RT has a large positive bias in the cold season from September to April, while CMORPH has a large positive bias in the warm season from May to August, and they all attained their best values in 2006 （with 10%, 50%, and -5% biases for 3B42V6, 3B42RT, and CMORPH, respectively）. Our evaluation shows that, for the Laohahe Basin, 3B42V6 has the best correspondence with the surface observations, and CMORPH performs much better than 3B42RT. The large errors of 3B42RT and CMORPH remind us of the need for new improvements to satellite precipitation retrieval algorithms or feasible bias adjusting methods.

Refined Evaluation of Satellite Precipitation Products against Rain Gauge Observations along the Sichuan–Tibet Railway

Being constructed in southwestern China, the Sichuan–Tibet Railway(STR) travels across the eastern Tibetan Plateau where there is the most complex terrain and changeable weather in the world. Due to sparse ground-based observations over the Tibetan Plateau, precipitation products retrieved by remote sensing are more widely used;however,satellite-based precipitation products(SPPs) have not yet been strictly and systematically evaluated along the STR.This study aims to evaluate the performance of six SPPs by a series of metrics with available ground observations along the STR during 1998–2020. The six SPPs include the datasets derived from the Tropical Rainfall Measuring Mission(TRMM), Climate Prediction Center morphing technique(CMORPH), Global Precipitation Measurement(GPM), Global Satellite Mapping of Precipitation(GSMaP), Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks(PERSIANN), and Fengyun-2 satellites precipitation estimate(FY2PRE). The results indicate that most of the SPPs can capture the precipitation characteristics on multiple timescales(monthly,daily, hourly, and diurnal cycle) as shown by the evaluated metrics. The probability density functions of the daily and hourly precipitation are also well represented by the SPPs, and 30 mm day^(-1) and 16 mm h^(-1) are identified as the daily and hourly thresholds of extreme precipitation events along the STR. The best SPP varies at different timescales:GPM and GSMaP are suitable for the monthly and daily scale, and FY2PRE and GPM are suited to the hourly scale.In general, GPM is relatively optimum on multiple timescales, and PERSIANN gives the worst performance. In addition, the SPPs perform worse at higher altitudes and for more intense precipitation. Overall, the results from this study are expected to provide essential reference for using the SPPs in meteorological services and disaster prevention in the STR construction and its future operation.

Rainfall Estimation using Image Processing and Regression Model on DWR Rainfall Product for Delhi-NCR Region

Observed rainfall is a very essential parameter for the analysis of rainfall,day to day weather forecast and its validation.The observed rainfall data is only available from five observatories of IMD;while no rainfall data is available at various important locations in and around Delhi-NCR.However,the 24-hour rainfall data observed by Doppler Weather Radar(DWR)for entire Delhi and surrounding region(up to 150 km)is readily available in a pictorial form.In this paper,efforts have been made to derive/estimate the rainfall at desired locations using DWR hydrological products.Firstly,the rainfall at desired locations has been estimated from the precipitation accumulation product(PAC)of the DWR using image processing in Python language.After this,a linear regression model using the least square method has been developed in R language.Estimated and observed rainfall data of year 2018(July,August and September)was used to train the model.After this,the model was tested on rainfall data of year 2019(July,August and September)and validated.With the use of linear regression model,the error in mean rainfall estimation reduced by 46.58% and the error in max rainfall estimation reduced by 84.53% for the year 2019.The error in mean rainfall estimation reduced by 81.36% and the error in max rainfall estimation reduced by 33.81%for the year 2018.Thus,the rainfall can be estimated with a fair degree of accuracy at desired locations within the range of the Doppler Weather Radar using the radar rainfall products and the developed linear regression model.

Analysis of Factors Influencing Shut in Pressure Cone in Offshore Strong Bottom Water Reservoir

X oilfield is an offshore strong bottom water reservoir with water cut up to 96% at present, and liquid extraction has become one of the main ways to increase oil production. However, the current liquid production of the oilfield reaches 60,000 m3/d due to the limitation of offshore platform, well trough and equipment, the oilfield is unable to continue liquid extraction. In order to maximize the oil production of the oilfield, it is necessary to study the strategy of shut in and cone pressure. Through numerical simulation, this paper analyzes the influence of different factors, such as crude oil density, viscosity, reservoir thickness, interlayer, permeability and so on, on the drop height of water cone and the effect of precipitation and oil increase after well shut in. At the same time, the weight of each factor is analyzed by combining the actual dynamic data with the fuzzy mathematics method, and the strategy of well shut in and cone pressure is formulated for the offshore strong bottom water reservoir. It provides the basis and guidance for the reasonable use of shut in pressure cone when the reservoir with strong bottom water meets the bottleneck of liquid volume.