Validation of COSMO model 24 h forecast rainfall using rain gauges estimated precipitation during occurrence of Typhoon Santi

Transcription

1 African Journal of Agricultural Science and Technology (AJAST) Vol. 3, Issue 6, pp June, ISSN Oceanic Journals Full Length research Paper Validation of COSMO model 24 h forecast rainfall using rain gauges estimated precipitation during occurrence of Typhoon Santi Lorenzo A. Moron*, Ma. Cecilia A. Monteverde, Lourdes R. Sulapat and Cynthia P. Celebre Department of Science and Technology, Philippine Atmospheric, Geophysical, Philippine Atmospheric Geophysical and Astronomical Services Administration (PAGASA), Republic of the Philippines. engr.oznerol@yahoo.com. Accepted 27 th May, 2015 This study aims to conduct verification of the COSMO model used for weather forecasting. To do this, an estimated 24 h forecast precipitation from the COSMO model is compared or correlated with the 24 h accumulated rainfall data from rain gauge networks using a point-rainfall interpolation method and kriging process. An ordinary kriging process on the differences between the 24 h COSMO model rainfall forecast and the rain gauge measurement is vital for modification purposes. Relative error is also computed upon modification of the precipitation field. This validation utilizes a sample case study on the occurrence of Typhoon Santi (international name Nari ) on October 08-13, 2013, which hit and devastated most of the provinces in Luzon. INTRODUCTION Accurate and reliable weather forecasting is an integral part of any national meteorological-hydrological agency. With the aid of various meteorological technologies, all these things are made possible. In weather forecasting, different tools and forecasting models are used thereby contributing towards sustainable hydro-meteorological services. On the other hand, validation of these meteorological tools is imperative to ensure efficient and accurate delivery of services. COSMO, being used for operation and research applications, offers wide scope and variety of applications to meteorological technology. For instance, in Europe, COSMO is one of the several consortia dealing with the development, evaluation and improvement of a limited area model for numerical weather prediction. It has been developed to meet highresolution regional forecast requirements of weather services and to provide a flexible tool for various scientific applications on a broad range of spatial scales. It has been designed for both operational Numerical Weather Prediction (NWP) and various scientific applications on the meso-β and meso-γ scale. One of the features of COSMO model is the rainfall forecast. This provides an estimated rainfall in a particular region in a color coded pattern depending on the intensity of precipitation. Rainfall can be brought about by either large-scale or localized weather system. Large scale or synoptic systems include tropical cyclone, monsoons (southwest and northeast monsoons), inter-tropical convergence zone and frontal systems while small-scale or localized systems include thunderstorms or any short-duration weather systems. All of these systems bring rainfall in a specific area or region which may cause hydro-meteorological hazards such as flashfloods, flooding and landslides. Although COSMO is said to have various applications and special features for operational numerical weather predictions, it also poses notable weaknesses. According to Rotach et al. (2008) in terms of performance, the most severe weakness for COSMO model is certainly the precipitation forecast. Although the introduction of prognostic precipitation variables has considerably improved the performance (especially in the lee of mountains) the COSMO Model still strongly

2 Afr. J. Agric. Sci. Technol. 287 overestimates precipitation, especially in winter and over mountains. In summer magnitudes are better, but the timing is weak. As an aid to better forecast the rainfall and ensure accuracy on the image generated by the COSMO model, this study provides validation of the COSMO model estimated rainfall using the actual observations on the ground provided by the rain gauge networks. Study area This study covers portion of Luzon, as shown in Figure 1, which were directly affected during the passage of typhoon Santi. Figure 2 shows the track of Typhoon Santi which crossed central Luzon and brought moderate to heavy with occasional intense rainfall to central Luzon, portions of northern Luzon, Metro Manila and Southern Luzon. Rainfall data were taken from twenty (20) different synoptic stations in the study area such as Aparri, Tuguegarao (Cagayan), Iba (Zambales), Dagupan City (Pangasinan), Clark Field (Pampanga), Baguio City (Benguet), Cabanatuan City (Nueva Ecija), Baler, Casiguran (Aurora), Subic (Zambales), Science Garden, Port Area, NAIA (Metro Manila), Sangley Point (Cavite), Tanay (Rizal), Tayabas, Infanta, Alabat (Quezon), Ambulong (Batangas) and, Calapan (Mindoro). Definition of terms For better and clearer understanding on this study, the following terms are defined conceptually and theoretically. Consortium for small-scale modeling (COSMO): a consortium of some national meteorological services, which aims to develop, improve and maintain a nonhydrostatic limited-area atmospheric prediction model, to be used both for operational and for research applications. Kriging: an alternative to many other point interpolation techniques based on statistical method. Kriging gives an interpolated map and output error map with the standard errors of the estimates. Correlation: is a single number that describes the degree of relationship between two variables. The correlation in this study describes the relationship between the COSMO model 24 h rainfall forecast and the 24 h observed rainfall through statistical treatment. Occurrence CASE STUDY: OCTOBER 9-13, 2013 DURING TYPHOON SANTI OCCURRENCE The wrath of Typhoon Santi, the 19 th tropical cyclone to hit the Philippines, left catastrophic damages in most of the provinces in Luzon. It entered the Philippine Area of Responsibility on October 8 and left PAR on October 13. It made landfall over Dingalan, Aurora at 11:00 PM on October 11, With massive destructions of typhoon Santi and the widespread advective precipitation it brought, this case study has been chosen. VALIDATION PROCEDURE Rainfall data: the rain gauges and the COSMO model mosaic There are about 57 manual synoptic rain gauge stations unevenly distributed throughout the Philippine Archipelago. The 24 h forecast rainfall data from the COSMO model are used together with those measured by the rain gauges belonging to the different PAGASA Regional Centers and made available through the PAGASA central office. In the Philippines, there are nine (9) operational radars, six (6) of which are managed and operated by PAGASA Baguio, Subic, Tagaytay, Mactan, Hinatuan, Baler and Tampakan radar stations while three (3) are installed by JICA Aparri, Virac and Guiuan radar stations. Reflectivity derived from the radar with some other rainfallmeasuring devices is assimilated by COSMO model into a numerical weather prediction (NWP) with certain resolution. Figure 3 shows the rain gauge distribution and the COSMO model rainfall mosaic. Table 1 depicts the tabulated 24 h rainfall data from rain gauge STATIONS and COSMO model. Point-rainfall interpolation procedure This part will determine correlation between rain gauge measurements and COSMO 24 h forecast rainfall. The main result of correlation is correlation coefficient and to find the correlation coefficient, the Pearson Product- Moment Correlation coefficient formula was used: Relative error: gives an indication of how good the measurement is relative to the size of the sample being measured.

3 Moron et al. 288 Figure 1. Map of portion of Luzon affected by TY Santi Where: R = correlation coefficient, n = total number of sample pairs, x = individual values of rain gauge measurements, y = individual values of COSMO model estimated 24 h rainfall. Note: Correlation coefficient ranges from -1.0 to The closer R is to +1 or -1, the more closely the two sets of data. If R is close to 0, it means there is no relationship between the variables. If R is (+), it means that as one data set gets larger, the other gets larger too. If R is (-), it means that as one gets larger, the other gets smaller, often called an inverse correlation. The square of the coefficient (or R-square) is equal to the percent of the variation in one data set that is related to the other data set. Figure 4 shows the correlation between the 24 h rainfall accumulated rain gauge measurements and the 24 h forecast rainfall of the COSMO model. Y = x R 2 = R = Calculation of the difference between cosmo and rain gauges The difference between the estimated 24 h cumulative rainfall from the COSMO model and rain gauges is computed. Point rainfall approach is used in which, the rainfall from the specific rain gauge station is correlated

4 Afr. J. Agric. Sci. Technol. 289 Figure 2. Track of TY Santi. Figure 3. Rain gauge network distribution (left) and the COSMO model rainfall mosaic (right). with the corresponding Cosmo 24 h forecast rainfall data on the same location. The difference or deviation between the two data is computed as follows:

5 Moron et al. 290 Table h rainfall data from rain gauge and 24 h rainfall forecast of COSMO model. STATION 24 H Accumulated rainfall 24 H Rainfall forecast (COSMO) Oct. 9 Oct. 10 Oct. 11 Oct. 12 Oct. 13 Oct. 9 Oct. 10 Oct. 11 Oct. 12 Oct. 13 APARRI TUGUEGARAO IBA DAGUPAN CITY CLARK FIELD BAGUIO CITY CABANATUAN CITY BALER RADAR CASIGURAN PORT AREA SUBIC BAY TAYABAS SANGLEY POINT NAIA SCIENCE GARDEN CALAPAN AMBULONG TANAY INFANTA ALABAT Table 2. Equivalent difference between 24 h accumulated RR (rain gauge) and 24 h rainfall forecast of (COSMO). S/N LONG LAT STATION Difference between COSMO and Rain gauge Rainfall Data, (db) Oct. 9 Oct. 10 Oct. 11 Oct. 12 Oct APARRI TUGUEGARAO IBA DAGUPAN CITY CLARK FIELD BAGUIO CITY CABANATUAN CITY BALER RADAR CASIGURAN PORT AREA SUBIC BAY TAYABAS SANGLEY POINT NAIA SCIENCE GARDEN CALAPAN AMBULONG TANAY INFANTA ALABAT Where r is the precipitation estimated by the COSMO model and rg is the rain gauge measurement. The result is in decibel (db), expressing the relative difference between the two variables. Table 2 shows the equivalent difference of the individual values between rainfall from COSMO and rain gauge measurements. Note: Negative decibel value means rainfall from COSMO model is less than the rain gauge measurement while positive decibel value means rainfall from COSMO model is greater than the rain gauge measurement. Ordinary Kriging To compare the estimated precipitation field produced by COSMO Model and the rain gauges network data, an ordinary kriging technique is used. This technique is done using the Arcview software which allows to

6 Afr. J. Agric. Sci. Technol. 291 Figure 4. Correlation between 24 h accumulated RR (rain gauge) and 24 h rainfall forecast of (COSMO). Table 3. Computed relative error between the COSMO model relative forecast and 24 h accumulated rainfall. STATION Relative error (%) Oct. 9 Oct. 10 Oct. 11 Oct. 12 Oct. 13 APARRI TUGUEGARAO IBA DAGUPAN CITY CLARK FIELD BAGUIO CITY CABANATUAN CITY BALER RADAR CASIGURAN PORT AREA SUBIC BAY TAYABAS SANGLEY POINT NAIA SCIENCE GARDEN CALAPAN AMBULONG TANAY INFANTA ALABAT visualize, manage and analyze GIS data. The data from the actual observation through the rain gauge network were treated with an ordinary kriging process. Figure 5 shows the comparison between the 24 h rainfall forecast of COSMO model and the 24 h accumulated rainfall observed through the rain gauge network Relative error The relative error is calculated as follows: Where Erel is the relative error, F is the COSMO model forecast precipitation amount and O is the observed rainfall. Table 3 shows the equivalent relative error between the COSMO model 24 h rainfall forecast and the 24 h accumulated rainfall.

7 Moron et al. 292 Figure 5. COSMO model 24 h rainfall forecast (left) and 24 h observed rainfall (right). RESULTS AND ANALYSIS Point-rainfall interpolation The result of correlation analysis reveals correlation coefficient or 52.5% coefficient of determination or equivalent to 27.56% of variation. According to Richard Taylor s Interpretation of the Correlation Coefficient, correlation coefficients which are 0.35 are generally considered to represent low or weak correlation, 0.36 to 0.67 modest or moderate correlation, and 0.68 to 1.0 strong or high correlations,

8 Afr. J. Agric. Sci. Technol. 293 with R coefficients.90 very high correlations. Thus, there is a moderate correlation between the COSMO model 24 h forecast and the 24 h observed rainfall. Ordinary Kriging There is an observable disparity on the output produced by the COSMO model with that of the actual observation. The overall mean difference between the COSMO model and rain gauge rainfall measurements ranges from +2.5dB to +5dB which mean that the COSMO model seems to overestimate the precipitation measured by the rain gauge. However, this value only gives the general scenario on the totality of the mean difference on the COSMO model and rain gauge measurements. This, perhaps, differs due to topographic differences leading to variability of rainfall measurements. As depicted in Table 2, COSMO model, at a particular time and location, tends to overestimate or underestimate the rain gauge measurement. The relative error also shows that there is a great disparity and variability on the values between the 24 h COSMO model rainfall forecast and the 24 h observed rainfall. RECOMMENDATION Although COSMO model possesses full physical parameterizations suited for weather prediction applications, the result of this study shows that the correlation on the COSMO model precipitation forecast and the observed value is not that highly reliable. Indeed, other literatures also reveal weaknesses of COSMO model to precipitation forecast. Similarly, a working group on verification and case studies relative to COSMO model also presented weaknesses of COSMO model to precipitation forecast, as depicted on their published No. 7 newsletter for Consortium for Small-Scale Modelling on April Other applications and special features of this tool may have an excellent performance however; its precipitation forecast feature is not a good reference to rainfall forecasting. Further researches should be conducted to investigate which assimilation is appropriate to weather forecasting. Efforts will have to be made to better exploit COSMO model forecast rainfall in order to diagnose and improve the forecast. Moreover, it is recommended to conduct similar studies on the validation of the COSMO model rainfall forecast using more meteorological events causing widespread precipitation. REFERENCES Richard Taylor (1990). Interpretation of the Correlation Coefficient: A Basic Review. Journal of Diagnostic Medical Sonography. 6: Rotach M. et al. (2008). Strategy for COSMO. pp /wg5/vela1400.pdf