POST PROCESSING OF MM5 FORECASTS FOR MINIMUM AND MAXIMUM TEMPERATURE USING A KALMAN FILTER

Q: How can I download an article?

To download an article from SID, first log in to the site, search for the article title, and click on the 'Download Article' option.

Q: How can I download an ISI article?

To download an ISI article on SID, enter the keyword or article title in the search bar, view the relevant results, click on the desired article, and select the 'Download Article' option.

Q: How can I access the SID database?

To access the SID database, visit SID.ir, create an account, and log in to access scientific resources.

Q: Is downloading articles from SID free?

Some articles on SID are available for free, while others require payment. Details are specified on the article's page.

AZADI M.; JAFARI SOMAYEH; MIRZAEI EBRAHIM; ARABLI PARVIN

Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Journal Paper

Paper Information

Journal: Journal of the Earth and Space Physics Year:2008 | Volume:34 | Issue:1 Page(s): 45-61

Download Full-Text

مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Persian Verion

View:

1,059

Download:

Cites:

Information Journal Paper

Title

POST PROCESSING OF MM5 FORECASTS FOR MINIMUM AND MAXIMUM TEMPERATURE USING A KALMAN FILTER

Author(s)

AZADI M. | JAFARI SOMAYEH | MIRZAEI EBRAHIM | ARABLI PARVIN | Issue Writer Certificate

Keywords

KALMAN FILTERINGQ3

POST PROCESSINGQ3

MAXIMUM AND MINIMUM TEMPERATUREQ3

Abstract

Direct numerical weather prediction model forecasts of near surface parameters often suffer from systematic errors mainly due to the low resolution of the model topography and inaccuracies in the physical parameterization schemes incorporated in the model. In this paper a simple objective algorithm based on KALMAN FILTERING have been implemented to correct the MAXIMUM AND MINIMUM TEMPERATURE model forecasts.In the last couple of years different methods for a postprocessing the model outputs have been developed. Kalman filter is one of them which provides a practical tool that combines the observed data and predictions of the model using a simple algorithm to reduce the systematic errors of the direct model outputs without the need for long historical data archives.This paper is organized as follows. In Section 2, we introduce a simple Kalman filter. In Section 3 and 4, we show that how the filter is applied on the model outputs for 2 meter minimum and maximum temperature for 117 meteorological stations. In Section 5, statistical results are presented and finally the paper is concluded in section 6. Simple Kalman Filter: The Kalman filter theory provides equations for recursively updating estimates of an unknown process through combining observations related to the process and time evolution of the process. Let xt be a vector describing the state of the unknown process at time t that, in this paper, is considered to be the systematic deviation between the observed and predicted temperatures. The state vector at time t is related to the state at time t−1 through the system equation: xt=ft.xt-1+wt where Ft describe the systematic change in xt and wt denotes the random part of the evolution of t x from time t−1 to time t and is known as the noise vector of the process. The state xt is related to the observation(s) yt through the observation equation: yt=ht.xt+vtwhere Ht is the observation matrix and vt is the noise vector of the observed data. wt and vt are assumed to be Gaussian white noise with zero mean processes and to have covariance matrixes Q and R respectively. Kalman Filter has two main steps; first step includes predictor equations which preestimate the state and its corresponding error covariance matrix:xt/t-1 = Ft . Xt-1 Pt/t-1 = Ft. Pt-1 .FTt +Q xt/t-1 is the pre-state and P is its error covariance matrix. The next step includes the corrector equations which update the pre-state using recent observation: xt = xt/t-1 + Kt (yt-Ht.xt/t-1) Kt = pt/t-1 .Ht/Ht .pt/t-1. HTt+R Pt= (I-H .Kt) Pt/t-1 where kt is Kalman gain. Procedure: Since there is not sufficient information about the dynamics of the system, a number of simplifying assumptions have been made; we consider F and H as constant unit matrixes.Estimates of initial state x0 and P0 and also wt and vt are required before running the filter. Initial values of x0 and P0 are not effective in the filter performance after some iterations and their consequence is lost. But the main problem in applying the filter is determination of noise vectors, wt and vt. The method proposed by Galanis and Anadranistakis for calculating wt and vt is used here and a training period of seven days was selected to train the filter using outputs of the MM5 modeling system. The Kalman filter was applied on model outputs for minimum and maximum temperature forecasts for 117 meteorological stations over Iran during 120 days and some statistical scores were calculated.

Cites

No record.

References

No record.

Cite

APA: Copy

AZADI, M., JAFARI, SOMAYEH, MIRZAEI, EBRAHIM, & ARABLI, PARVIN. (2008). POST PROCESSING OF MM5 FORECASTS FOR MINIMUM AND MAXIMUM TEMPERATURE USING A KALMAN FILTER. JOURNAL OF THE EARTH AND SPACE PHYSICS, 34(1), 45-61. SID. https://sid.ir/paper/80363/en

Vancouver: Copy

AZADI M., JAFARI SOMAYEH, MIRZAEI EBRAHIM, ARABLI PARVIN. POST PROCESSING OF MM5 FORECASTS FOR MINIMUM AND MAXIMUM TEMPERATURE USING A KALMAN FILTER. JOURNAL OF THE EARTH AND SPACE PHYSICS[Internet]. 2008;34(1):45-61. Available from: https://sid.ir/paper/80363/en

IEEE: Copy

M. AZADI, SOMAYEH JAFARI, EBRAHIM MIRZAEI, and PARVIN ARABLI, “POST PROCESSING OF MM5 FORECASTS FOR MINIMUM AND MAXIMUM TEMPERATURE USING A KALMAN FILTER,” JOURNAL OF THE EARTH AND SPACE PHYSICS, vol. 34, no. 1, pp. 45–61, 2008, [Online]. Available: https://sid.ir/paper/80363/en

Related Journal Papers