Scheduling of Non-Stationary Hourly Precipitation

2013 ◽  
pp. 269-278 ◽  
Author(s):  
Robert N. Eli ◽  
Thomas E. Croley
Keyword(s):  
Hourly Precipitation

scholarly journals Atmospheric Thermal and Dynamic Vertical Structures of Summer Hourly Precipitation in Jiulong of the Tibetan Plateau

Atmosphere ◽  
2021 ◽  
Vol 12 (4) ◽  
pp. 505
Author(s):  
Yonglan Tang ◽  
Guirong Xu ◽  
Rong Wan ◽  
Xiaofang Wang ◽  
Junchao Wang ◽  
...  
Keyword(s):  
Wind Speed ◽  
Tibetan Plateau ◽  
Precipitation Amount ◽  
Summer Precipitation ◽  
The Tibetan Plateau ◽  
Hourly Precipitation ◽  
Air Convection ◽  
Precipitation Variation ◽  
Long Term Observation

It is an important to study atmospheric thermal and dynamic vertical structures over the Tibetan Plateau (TP) and their impact on precipitation by using long-term observation at representative stations. This study exhibits the observational facts of summer precipitation variation on subdiurnal scale and its atmospheric thermal and dynamic vertical structures over the TP with hourly precipitation and intensive soundings in Jiulong during 2013–2020. It is found that precipitation amount and frequency are low in the daytime and high in the nighttime, and hourly precipitation greater than 1 mm mostly occurs at nighttime. Weak precipitation during the daytime may be caused by air advection, and strong precipitation at nighttime may be closely related with air convection. Both humidity and wind speed profiles show obvious fluctuation when precipitation occurs, and the greater the precipitation intensity, the larger the fluctuation. Moreover, the fluctuation of wind speed is small in the morning, large at noon and largest at night, presenting a similar diurnal cycle to that of convective activity over the TP, which is conductive to nighttime precipitation. Additionally, the inverse layer is accompanied by the inverse humidity layer, and wind speed presents multi-peaks distribution in its vertical structure. Both of these are closely related with the underlying surface and topography of Jiulong. More studies on physical mechanism and numerical simulation are necessary for better understanding the atmospheric phenomenon over the TP.

scholarly journals An Improved Weighting Method of Time-Lag-Ensemble Averaging for Hourly Precipitation Forecasts and Its Application in a Typhoon-Induced Heavy Rainfall Event

Atmosphere ◽  
2021 ◽  
Vol 12 (7) ◽  
pp. 875
Author(s):  
Li Zhou ◽  
Lin Xu ◽  
Mingcai Lan ◽  
Jingjing Chen
Keyword(s):  
Heavy Rainfall ◽  
Time Lag ◽  
Probability Of Detection ◽  
Precipitation Forecast ◽  
Ensemble Averaging ◽  
Weighting Method ◽  
Prediction Ability ◽  
Rainfall Events ◽  
Hourly Precipitation ◽  
Heavy Rainfall Events

Heavy rainfall events often cause great societal and economic impacts. The prediction ability of traditional extrapolation techniques decreases rapidly with the increase in the lead time. Moreover, deficiencies of high-resolution numerical models and high-frequency data assimilation will increase the prediction uncertainty. To address these shortcomings, based on the hourly precipitation prediction of Global/Regional Assimilation and Prediction System-Cycle of Hourly Assimilation and Forecast (GRAPES-CHAF) and Shanghai Meteorological Service-WRF ADAS Rapid Refresh System (SMS-WARR), we present an improved weighting method of time-lag-ensemble averaging for hourly precipitation forecast which gives more weight to heavy rainfall and can quickly select the optimal ensemble members for forecasting. In addition, by using the cross-magnitude weight (CMW) method, mean absolute error (MAE), root mean square error (RMSE) and correlation coefficient (CC), the verification results of hourly precipitation forecast for next six hours in Hunan Province during the 2019 typhoon Bailu case and heavy rainfall events from April to September in 2020 show that the revised forecast method can more accurately capture the characteristics of the hourly short-range precipitation forecast and improve the forecast accuracy and the probability of detection of heavy rainfall.

scholarly journals Spatial analysis of hourly precipitation combining observations and ensemble forecasts

2021 ◽  
Author(s):  
Cristian Lussana ◽  
Thomas N. Nipen ◽  
Ivar A. Seierstad ◽  
Christoffer A. Elo
Keyword(s):  
Spatial Analysis ◽  
Gamma Distribution ◽  
Numerical Models ◽  
Spatial Scales ◽  
Data Sources ◽  
Optimal Interpolation ◽  
Interpolation Scheme ◽  
Background Error ◽  
Hourly Precipitation ◽  
Gaussian Anamorphosis

<p>Hourly precipitation is often simultaneously simulated by numerical models and observed by multiple data sources. Accurate precipitation fields based on all available information are valuable input for numerous applications and a critical aspect of climate monitoring. </p><p>Inverse problem theory offers an ideal framework for the combination of observations with a numerical model background. In particular, we have considered a modified ensemble optimal interpolation scheme. The deviations between background and observations are used to adjust for deficiencies in the ensemble. A data transformation based on Gaussian anamorphosis has been used to optimally exploit the potential of the spatial analysis, given that precipitation is approximated with a gamma distribution and the spatial analysis requires normally distributed variables. For each point, the spatial analysis returns the shape and rate parameters of its gamma distribution. </p><p>The ensemble-based statistical interpolation scheme with Gaussian anamorphosis for precipitation (EnSI-GAP) is implemented in a way that the covariance matrices are locally stationary, and the background error covariance matrix undergoes a localization process. Concepts and methods that are usually found in data assimilation are here applied to spatial analysis, where they have been adapted in an original way to represent precipitation at finer spatial scales than those resolved by the background, at least where the observational network is dense enough.</p><p>The EnSI-GAP setup requires the specification of a restricted number of parameters, and specifically, the explicit values of the error variances are not needed, since they are inferred from the available data. </p><p>The examples of applications presented over Norway provide a better understanding of EnSI-GAP. The data sources considered are those typically used at national meteorological services, such as local area models, weather radars, and in situ observations. For this last data source, measurements from both traditional and opportunistic sensors have been considered.</p>

scholarly journals Global distribution of the intensity and frequency of hourly precipitation and their responses to ENSO

2020 ◽  
Vol 54 (11-12) ◽  
pp. 4823-4839
Author(s):  
Xiao-Feng Li ◽  
Stephen Blenkinsop ◽  
Renaud Barbero ◽  
Jingjing Yu ◽  
Elizabeth Lewis ◽  
...  
Keyword(s):  
Global Distribution ◽  
Hourly Precipitation
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