precipitation forecasting
Recently Published Documents


TOTAL DOCUMENTS

211
(FIVE YEARS 46)

H-INDEX

33
(FIVE YEARS 4)

MAUSAM ◽  
2021 ◽  
Vol 42 (2) ◽  
pp. 201-204
Author(s):  
P. N. SEN

A mathematical, model for Quantitative Precipitation Forecasting (QPF) has been developed on the basis of physical and dynamical laws. The surface and upper air meteorological observations have been used as inputs in the model. The output is the rate of precipitation from which the amount of precipitation can be computed time integration. The model can be used operationally for rainfall forecasting.


MAUSAM ◽  
2021 ◽  
Vol 47 (4) ◽  
pp. 349-354
Author(s):  
Y.E. A. RAJ ◽  
JAYANTA SARKAR ◽  
B. RAMAKRISHNAN

Quantitative precipitation forecasting (QPF) of daily rainfall of Thiruvananthapuram and Madras  for June-September and October-December respectively for the year 1992 has been attempted. A mathematical model of QPF based on the concept of conservation of specific humidity and with upper air data of a network of stations as the data input has been employed. Nearly 66% and 72% correct forecasts were realised respectively for the two stations. Scope for further refinement has been briefly discussed.    


MAUSAM ◽  
2021 ◽  
Vol 72 (4) ◽  
pp. 781-790
Author(s):  
MAHBOOB ALAM ◽  
MOHD. AMJAD

Numerical weather prediction (NWP) has long been a difficult task for meteorologists. Atmospheric dynamics is extremely complicated to model, and chaos theory teaches us that the mathematical equations used to predict the weather are sensitive to initial conditions; that is, slightly perturbed initial conditions could yield very different forecasts. Over the years, meteorologists have developed a number of different mathematical models for atmospheric dynamics, each making slightly different assumptions and simplifications, and hence each yielding different forecasts. It has been noted that each model has its strengths and weaknesses forecasting in different situations, and hence to improve performance, scientists now use an ensemble forecast consisting of different models and running those models with different initial conditions. This ensemble method uses statistical post-processing; usually linear regression. Recently, machine learning techniques have started to be applied to NWP. Studies of neural networks, logistic regression, and genetic algorithms have shown improvements over standard linear regression for precipitation prediction. Gagne et al proposed using multiple machine learning techniques to improve precipitation forecasting. They used Breiman’s random forest technique, which had previously been applied to other areas of meteorology. Performance was verified using Next Generation Weather Radar (NEXRAD) data. Instead of using an ensemble forecast, it discusses the usage of techniques pertaining to machine learning to improve the precipitation forecast. This paper is to present an approach for mapping of precipitation data. The project attempts to arrive at a machine learning method which is optimal and data driven for predicting precipitation levels that aids farmers thereby aiming to provide benefits to the agricultural domain.


Atmosphere ◽  
2021 ◽  
Vol 12 (10) ◽  
pp. 1253
Author(s):  
Hongxiang Ouyang ◽  
Zhengkun Qin ◽  
Juan Li

Assimilation of high-resolution geostationary satellite data is of great value for precise precipitation prediction in regional basins. The operational geostationary satellite imager carried by the Himawari-8 satellite, Advanced Himawari Imager (AHI), has two additional water vapor channels and four other channels compared with its predecessor, MTSAT-2. However, due to the uncertainty in surface parameters, AHI surface-sensitive channels are usually not assimilated over land, except for the three water vapor channels. Previous research showed that the brightness temperature of AHI channel 16 is much more sensitive to the lower-tropospheric temperature than to surface emissivity, which is similar to the three water vapor channels 8–10. As a follow-up work, this paper evaluates the effectiveness of assimilating brightness temperature observations over land from both the three AHI water vapor channels and channel 16 to improve watershed precipitation forecasting through both case analysis (in the Haihe River basin, China) and batch tests. It is found that assimilating AHI channel 16 can improve the upstream near-surface atmospheric temperature forecast, which in turn affects the development of downstream weather systems. The precipitation forecasting test results indicate that adding the terrestrial observations of channel 16 to the assimilation of AHI data can improve short-term precipitation forecasting in the basin.


2021 ◽  
Vol 2 (1-4) ◽  
Author(s):  
Cyrille Flamant ◽  
Patrick Chazette ◽  
Olivier Caumont ◽  
Paolo Di Girolamo ◽  
Andreas Behrendt ◽  
...  

2021 ◽  
Author(s):  
Lei Xu ◽  
Nengcheng Chen ◽  
Chao Yang

Abstract. Precipitation forecasting is an important mission in weather science. In recent years, data-driven precipitation forecasting techniques could complement numerical prediction, such as precipitation nowcasting, monthly precipitation projection and extreme precipitation event identification. In data-driven precipitation forecasting, the predictive uncertainty arises mainly from data and model uncertainties. Current deep learning forecasting methods could model the parametric uncertainty by random sampling from the parameters. However, the data uncertainty is usually ignored in the forecasting process and the derivation of predictive uncertainty is incomplete. In this study, the input data uncertainty, target data uncertainty and model uncertainty are jointly modeled in a deep learning precipitation forecasting framework to estimate the predictive uncertainty. Specifically, the data uncertainty is estimated a priori and the input uncertainty is propagated forward through model weights according to the law of error propagation. The model uncertainty is considered by sampling from the parameters and is coupled with input and target data uncertainties in the objective function during the training process. Finally, the predictive uncertainty is produced by propagating the input uncertainty and sampling the weights in the testing process. The experimental results indicate that the proposed joint uncertainty modeling and precipitation forecasting framework exhibits comparable forecasting accuracy with existing methods, while could reduce the predictive uncertainty to a large extent relative to two existing joint uncertainty modeling approaches. The developed joint uncertainty modeling method is a general uncertainty estimation approach for data-driven forecasting applications.


Eos ◽  
2021 ◽  
Vol 102 ◽  
Author(s):  
Michael DeFlorio ◽  
F. Ralph ◽  
Duane Waliser ◽  
Jeanine Jones ◽  
Michael Anderson

Emerging methods that improve precipitation forecasting over weeks to months could support more informed resource management and increase lead times for responding to droughts and floods.


Sign in / Sign up

Export Citation Format

Share Document