Daily rainfall forecasts through a quantitative precipitation forecasting (QPF) model over Thiruvananthapuram and Madras areas for the monsoons of 1992

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.

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Estimation of Short-Duration Rainfall Distribution Using Data Measured at Longer Time Scales

Water Science & Technology ◽

10.2166/wst.1994.0649 ◽

1994 ◽

Vol 29 (1-2) ◽

pp. 39-45 ◽

Cited By ~ 1

Author(s):

Van-Thanh Van Nguyen ◽

Ganesh Raj Pandey

Keyword(s):

Mathematical Model ◽

Probability Distribution ◽

Time Scales ◽

Short Duration ◽

Daily Rainfall ◽

Regional Model ◽

Rainfall Data ◽

Rainfall Distribution ◽

Proposed Model ◽

Using Data

An investigation on how to estimate the distribution of short-duration (hours or shorter) rainfalls based on available daily rainfall measurements was undertaken. On the basis of the theory of multifractal multiplicative cascades, a scale-independent mathematical model was proposed to represent the probability distribution of rainfalls at various time scales. Using rainfall records from a network of seven recording gauges in the Montreal region in Quebec (Canada), it was found that the proposed model could provide adequate estimates of the distribution of hourly rainfalls at locations where these short-duration rainfall data are not available. Further, it has been observed that one single regional model can be developed to describe the scaling nature of rainfall distributions within the whole study area.

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Development of Modified Incompressible Ideal Gas Model for Natural Draft Cooling Tower Flow Simulation

EPJ Web of Conferences ◽

10.1051/epjconf/201818002037 ◽

2018 ◽

Vol 180 ◽

pp. 02037

Author(s):

Tomáš Hyhlík

Keyword(s):

Mathematical Model ◽

Mass Transfer ◽

Heat And Mass Transfer ◽

Cooling Tower ◽

Flow Simulation ◽

Ideal Gas ◽

Specific Humidity ◽

Boussinesq Model ◽

Natural Draft ◽

Ideal Gas Model

The article deals with the development of incompressible ideal gas like model, which can be used as a part of mathematical model describing natural draft wet-cooling tower flow, heat and mass transfer. It is shown, based on the results of a complex mathematical model of natural draft wet-cooling tower flow, that behaviour of pressure, temperature and density is very similar to the case of hydrostatics of moist air, where heat and mass transfer in the fill zone must be taken into account. The behaviour inside the cooling tower is documented using density, pressure and temperature distributions. The proposed equation for the density is based on the same idea like the incompressible ideal gas model, which is only dependent on temperature, specific humidity and in this case on elevation. It is shown that normalized density difference of the density based on proposed model and density based on the nonsimplified model is in the order of 10-4. The classical incompressible ideal gas model, Boussinesq model and generalised Boussinesq model are also tested. These models show deviation in percentages.

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Predictions of ice-cover development in streams and its effect on dissolved oxygen modelling

Canadian Journal of Civil Engineering ◽

10.1139/l79-020 ◽

1979 ◽

Vol 6 (2) ◽

pp. 197-207 ◽

Cited By ~ 4

Author(s):

E. McBean ◽

G. Farquhar ◽

N. Kouwen ◽

O. Dubek

Keyword(s):

Mathematical Model ◽

Water Quality ◽

Dissolved Oxygen ◽

Prediction Model ◽

Data Input ◽

Quality Model ◽

Two Stage ◽

Second Stage ◽

Series Of Experiments ◽

Ice Edge

A two-stage mathematical model is developed for predicting dissolved oxygen levels in ice-covered rivers. The first stage of the model is a prediction model for ice-edge progression as a function of time, and the second stage consists of an extrapolation of a widely used 'summer condition' water-quality model. The results of a series of experiments, both field and laboratory-based, which served as data input generators and calibration testing of the model, are provided.Briefcase-study applications of elements of the model to the Speed River and to the Saint John River are included.

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A mathematical model for QPF for flood Forecasting purposes

MAUSAM ◽

10.54302/mausam.v42i2.3152 ◽

2021 ◽

Vol 42 (2) ◽

pp. 201-204

Author(s):

P. N. SEN

Keyword(s):

Mathematical Model ◽

Time Integration ◽

Flood Forecasting ◽

Rainfall Forecasting ◽

Precipitation Forecasting ◽

Meteorological Observations

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.

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Simulation of Daily Rainfall from Concurrent Meteorological Parameters over Core Monsoon Region of India: A Novel Approach

Advances in Meteorology ◽

10.1155/2018/3053640 ◽

2018 ◽

Vol 2018 ◽

pp. 1-18

Author(s):

Utpal Misra ◽

Atri Deshamukhya ◽

Sanjay Sharma ◽

Srimanta Pal

Keyword(s):

Meteorological Parameters ◽

Good Accuracy ◽

Daily Rainfall ◽

Meridional Wind ◽

Specific Humidity ◽

Monsoon Region ◽

Intraseasonal Variation ◽

Coupled Models ◽

Novel Approach ◽

Mlp Model

In the present work, daily rainfall is simulated over the core monsoon region of India by using a feedforward multilayer perceptron (MLP) model. Daily rainfall is found to be optimally dependent on four concurrent meteorological parameters, namely, geopotential height, specific humidity, zonal, and meridional wind at 1000 mb, 925 mb, 850 mb, and 700 mb pressure levels during 00, 06, 12, and 18 Greenwich Mean Time (GMT). The architecture of the optimized feedforward MLP model consists of 64 nodes in the input layer, 10 nodes in the hidden layer, and 1 node in the output layer. The results from the model are compared with the 3B42 (version 7) rainfall product. In terms of root mean square error (rmse) and correlation coefficient (cc), the model is performing better compared to the satellite-derived 3B42 rainfall product, whereas in terms of bias, the performance of the 3B42 product is better compared to the model. The weight matrices of the feedforward MLP model are estimated at a particular location (22.5°N, 82.5°E). These weight matrices are able to simulate daily rainfall at neighbourhood locations also with reasonably good accuracy with cc in the range of 0.41 to 0.55. The performance of the model improves in case of an aerial average of daily rainfall with significantly enhanced cc (0.72). The model is able to capture monthly and intraseasonal variation of rainfall with reasonably good accuracy, with cc of 0.88 and 0.68, respectively. The simulation model has a limitation that it is not able to simulate extreme high rainfall events (>60 mm/day). Overall, the developed model is performing reasonably well. This approach has a potential to be used as a rain parameterization scheme in the dynamical atmospheric and coupled models to simulate daily rainfall. Nevertheless, the present approach can also be used for multistep prediction of rainfall.

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Statistical significance of trend estimations for integrated water vapor time series obtained from GPS technique: a case study in Europe

10.5194/egusphere-egu2020-11728 ◽

2020 ◽

Author(s):

Peng Yuan ◽

Addisu Hunegnaw ◽

Felix Norman Teferle ◽

Hansjörg Kutterer

Keyword(s):

Mathematical Model ◽

Time Series ◽

Global Warming ◽

Water Vapor ◽

Statistical Significance ◽

Weather Condition ◽

Noise Model ◽

Specific Humidity ◽

High Temporal Resolution ◽

Total Delay

<p>Water vapor is an important medium for the transmission moisture and latent heat in the atmosphere. It is one of the most abundant and dominant greenhouse gases in the atmosphere, which is crucial for global warming. With higher temperatures, the specific humidity will also increase as predicted by the nonlinear Clausius-Clapeyron relationship, indicating a positive feedback loop. Hence, estimation of the trend of Integrated Water Vapor (IWV) in the atmosphere is of great importance for global warming research. However, previous studies have shown that the trends of IWV are usually rather small. Therefore, it is important to estimate the IWV trend and its associated uncertainty with a reasonable mathematical model for the homogenized time series from homogenously reprocessed GPS data sets. Since the 1990s, the Global Positioning System (GPS) has successfully been employed to retrieve IWV with a high temporal resolution, all-weather condition and with global coverage. In this work, we used the hourly GPS Zenith Total Delay (ZTD) time series for 1995.0-2017.0 at 21 European GPS stations derived from a homogeneous data reprocessing. For the conversion of ZTD to IWV, we employed the meteorological variables from ERA5, a state-of-the-art atmosphere reanalysis product newly released by the European Centre for Medium-Range Weather Forecasts (ECMWF). Then, we investigated the influence of noise model assumptions within the mathematical model on the uncertainties of IWV trend estimates. As expected, the results confirmed that the assumption of a white noise only model tends to underestimate the trend uncertainty. A first-order autoregressive process is the preferred mathematical model for a more realistic estimation of the IWV trend uncertainty.</p>

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