WEATHER AND FORECASTING VOLUME Characterization of Weekly Cumulative Rainfall Forecasts over Meteorological Subdivisions of India Using a GCM S. A. SASEENDRAN,* S. V. SINGH, L.S.RATHORE, AND SOMESHWAR DAS National Centre for Medium Range Weather Forecasting, New Delhi, India (Manuscript received December, in final form December ) ABSTRACT Weekly cumulative rainfall forecasts were made for the meteorologically homogeneous areas of the Indian subcontinent, divided into meteorological subdivisions, by performing -day integrations of the operational Indian T Global Spectral Model every Wednesday during the six southwest monsoon seasons of. Objective evaluations of the bias and accuracy of these forecasts during that -yr period are made through various forecast verification methods and are presented here. The skill or relative accuracy of the forecasts and some verification measures are quantified by computing the Heidke skill score (HSS), Hanssen Kuipers discriminant (HKS), threat score (TS), hit rate (HR), probability of detection (POD), bias score, and false-alarm rate (FAR). The study revealed that the T model has a tendency to underpredict rainfall over most of the subdivisions falling on the windward side of the Western Ghats and sub-himalayan areas. The model exhibited negative bias in rainfall simulations over the desert regions of Rajasthan and over the Arabian Sea and bay islands. There is a positive bias in the rainfall simulated over the subdivisions falling in the rain-shadow regions of the Western Ghats. The TS, POD, and FAR computations show that the predicted weekly rainfall over different subdivisions in the excess and scanty categories has more skill than those in the normal and deficient categories. The HR values range from. to over different subdivisions. The HSS and HKS scores indicate better skill in rainfall forecast in the central belt of India where the orographic influence over rainfall distribution is comparatively less. Better correspondence between the magnitude of the predicted and observed rainfall is apparent in the all-india time series of weekly cumulative rainfall.. Introduction In the tropical regions of the world, water availability is the most important factor controlling crop growth. Under the rain-fed agricultural scenario, farmers have to depend solely on in situ rainfall for supporting crop production. Rainfall forecasts can help farmers and water management personnel in planning and scheduling agricultural operations to derive maximum benefit from precipitation. If rainfall events can be predicted to a degree of accuracy that makes it possible to respond effectively in the agricultural sector, it would potentially have a major impact on worldwide food resources. Farmers would be better prepared for rainfall anomalies and thus less vulnerable if such forecasts were made available in time. The Agrometeorological Advisory Service units of the Indian National Centre for Medium Range Weather Fore- * Current affiliation: Great Plains Systems Research, U.S. Department of Agriculture Agricultural Research Service, Fort Collins, Colorado. Corresponding author address: S. A. Saseendran, Great Plains Systems Research, USDA Agricultural Research Service, Fort Collins, CO. E-mail: anapalli@gpsr.colostate.edu casting (NCMRWF) can effectively disseminate such forecasts to the farming community and other end-users through use of their existing communication networks. Development of a procedure to obtain the weekly forecasts of cumulative rainfall is a prerequisite. Keeping this in view, the operational T Global Spectral Model of the NCMRWF was used to produce forecasts of weekly cumulative rainfall departures from normal for the meteorological subdivisions of India during the six southwest monsoon seasons of. The meteorological subdivisions are listed in Table. (Their locations are shown in Fig., described later.) Saseendran et al. () reported the behavior of cumulative subdivisional rainfall forecasts of the T model during the monsoon season of. The study did not show much correspondence between the quantity of rainfall forecast by the model and that observed on the subdivisional scale. Nonetheless, it was observed that the temporal fluctuations and trends in the observed rainfall are reflected well in the model forecasts. In the present investigation, skill scores and verification measures are computed to evaluate where, when, and how the forecast performs well among the meteorological subdivisions of India, so that the forecast can be utilized in decision making at a given subdivision with better con- American Meteorological Society
AUGUST SASEENDRAN ET AL. TABLE. List of meteorological subdivisions of India (see Fig. ). No. Subdivision Jammu and Kashmir Himachal Pradesh Punjab Haryana, Chandigarh, and Delhi Hills of west Uttar Pradesh Plains of west Uttar Pradesh East Uttar Pradesh Bihar plains Bihar plateau Gangetic West Bengal Sub-Himalayan West Bengal and Sikkim Assam and Meghalaya Arunachal Pradesh Nagaland, Manipur, Mizoram, and Tripura Orissa East Madhya Pradesh West Madhya Pradesh East Rajasthan West Rajasthan Saurashtra, Kutch, and Diu Gujarat region, Daman, Dadra, and Nagar Haveli Madhya Maharashtra Vidarbha Marathawada Konkan and Goa Telangana Coastal Andhra Pradesh Rayalaseema North interior Karnataka Coastal Karnataka South interior Karnataka Tamil Nadu and Pondicherry Kerala Andaman and Nicobar Islands Lakshadweep fidence. To reveal the error structure of the subdivisional weekly cumulative rainfall forecast in the different subdivisions of India during the monsoon seasons of, the Heidke skill score, Hanssen Kuipers discriminant, threat score, hit rate, probability of detection, bias score, and false-alarm rate are computed and presented.. Methodology a. Forecast model The general circulation model (GCM) used in the Medium Range Analysis Forecast System (MAFS) of the NCMRWF is a version adapted from the National Centers for Environmental Prediction (Sela ). It is a global spectral model having T horizontal resolution (about km) and layers in the vertical. The main features of the model are summarized in Table. The model uses climatological boundary conditions for the sea surface temperature, albedo, ice, snow, soil moisture, deep soil temperature, roughness length, and plant resistance. The MAFS operational at NCMRWF consists of ) data processing and quality control; ) utilization of nonconventional data such as cloud motion vectors, FIG.. The T model gridpoint locations over the various meteorological subdivisions of India. temperature, and moisture from the National Oceanic and Atmospheric Administration series of satellites, European Remote Sensing Satellites scatterometer data, and so on; ) data assimilation; ) model integration; ) postprocessing and diagnostic studies; and ) preparation of location-specific forecasts. b. Preparation of subdivisional weekly cumulative rainfall forecasts The operational T GCM is integrated up to days from the UTC initial conditions of each Wednesday. The forecasts thus produced correspond to the weeks considered by the India Meteorological Department (IMD) for preparation of the operational weekly weather reports (WWR). The IMD makes use of rain gauge data received daily from over rain gauge stations distributed all over the country for making the WWR. This rain gauge network adequately represents the varied topographical regions of the country. The weekly subdivisional rainfall forecasts for each of the subdivisions are obtained from the output of the model runs by preparing an area-weighted average of the weekly cumulative rainfall of the grid points overlying the respective subdivisions. The T model gridpoint distribution over the different meteorological subdivisions of the country is given in Fig.. The all-india weekly cumulative rainfall forecast is obtained by preparing a weighted average of subdivisional rainfall forecasts. The observed all-india weekly rainfall is obtained from the weekly subdivisional rainfall published by IMD in the WWR. For evaluating the performance of the weekly subdivisional-scale rainfall forecasts, the model-forecast rainfall quantity is converted into percentage of departures from normal by using the respective subdivisional
WEATHER AND FORECASTING VOLUME TABLE. Description of the NCMRWF Global Spectral Model. Model elements Components Specifications Grid Horizontal Global spectral-t ( ) Vertical sigma layers (.,.,.,.,.,.,.,.,.,.,.,.,.,.,.,.,.,.) Topography Prognostic variables Mean Relative vorticity, divergence, virtual temperature, log of surface pressure, water vapor mixing ratio Dynamics Horizontal transform Orszag s technique Vertical differencing Arakawa s energy-conserving scheme Time differencing Semi-implicit, s Time filtering Robert s method Horizontal diffusion Second-order over quasi pressure surface, scale-selective Physics Surface fluxes Monin Obukhov similarity theory Turbulent diffusion K theory Radiation Shortwave Lacis and Hansen; longwave Fels and Schwarzkopf Deep convection Kuo scheme modified Shallow convection Tiedtke s scheme Large-scale condensation Manabe s scheme Clouds Slingo s scheme Rain evaporation Kessler s scheme Land surface process Pan s (-layer soil temperature, bucket hydrology for soil moisture) scheme Air sea interaction Roughness length (Charnock), SST, SH, and LH (bulk formula) normal rainfall values. The forecast departures (%) are then categorized into five categories, according to the criteria as provided in Table. These categories are assigned different numbers (see last column of Table ) for facilitating comparison between observed and forecast rainfall. The forecast rainfall categories are then compared with the observed rainfall categories, obtained from WWR for each subdivision, for the weeks falling within the southwest monsoon seasons of. The weeks considered here correspond to the -day cycle, that is, from Thursday to Wednesday, as followed by IMD for the WWR. Only full weeks falling within the southwest monsoon period of June September are considered in the analysis. There were a total number of weeks during the yr of southwest monsoon seasons considered in this study. c. Verification approach Verification of the four categories of cumulative rainfall (Table ) forecasts was performed using the contingency table approach. This approach can serve as the starting point for examination of the strengths and weaknesses of the forecasts (Murphy and Winkler, ). All of the forecast verification methods investigate the properties of the joint distribution of forecasts TABLE. Criteria adopted for calculation of rainfall intensity forecast categories. Category Departure from normal Assigned No. Excess Normal Deficient Scanty % % to % % to % % to % and observations (Murphy and Winkler ). Objective evaluations of forecast quality are normally undertaken for a variety of purposes, such as scientific, administrative, and economic ones (e.g., Wilks ). In the work presented, a verification study was undertaken to identify how well the weekly cumulative rainfall forecasts, represented as categories of departure from normal, from the T model perform among the meteorological subdivisions of India. These forecasts can be utilized in weather-forecast based decision making for various applications with better confidence, by examining forecast skill for various scores available in the literature. A skill score is an index of the performance of a set of forecasts, expressed with reference to a particular standard such as forecasts based upon chance, persistence, or climatic means. A summary of many standard forecast verification methods and procedures can be found in Wilks (). ) DICHOTOMOUS FORECAST VERIFICATION For verification of the accuracy of forecasts using the hit rate and threat score, the four category forecasts (Table ) are verified individually, treating them as dichotomous in the individual forecast categories (excess, normal, deficient, and scanty). (i) Hit rate (HR) Hit rate is a measure of the accuracy of the forecast. This score is also known as the ratio score or the proportion correct. The hit rate provides a measure that is easy to interpret. However, it can be overwhelmed by a category that occurs much more frequently than the others. The Finley affair, as described by Murphy
AUGUST SASEENDRAN ET AL. () provides a good example of this drawback. The HR is calculated for dichotomous cases of forecast as HR (correct number of forecasts in both categories)/ (total number of forecasts). The score varies from to, with indicating the perfect forecast and representing no skill (Woodcock ). (ii) Bias score (BS) The bias score represents a comparison of the frequency of a particular forecast category with the frequency of the corresponding observation category, expressed as a ratio (Hughes ; Anthes ; Wilks ). Unbiased forecasts score BS, indicating that the event was forecast the same number of times it was observed. A bias score of less than indicates that the event was forecast fewer times than it was observed (underforecasting). A bias score of greater than indicates the event was forecast more frequently than it was actually observed (overforecasting). In the study presented, BS was calculated by subdivision. The numbers of yes forecasts in all the four categories for a particular subdivision were added together to make a single set of yes forecasts, and corresponding yes observations in the various categories were added together to make the yes observations set. The BS was computed as follows: BS (number of yes forecasts)/(number of yes observations). (iii) Probability of detection (POD) POD measures the success of the model in correctly forecasting the occurrence of rain in a particular category (Doswell et al. ; Wilks ), expressed as POD (correct forecasts in a particular category)/(total observations in that category). POD for perfect forecasts is, and the minimum POD is. (iv) False-alarm rate (FAR) FAR measures the fraction of predictions that were not actually observed (Doswell et al. ; Wilks ): FAR (incorrect forecasts or false alarms)/(total forecasts). FAR varies between and. For perfect forecasts FAR. (v) Threat score (TS) The TS or critical success index is a measure of relative forecasting accuracy (Anthes ; Doswell et al. ; Schaefer ). This score takes into account both false alarms and the missed events. It varies from to, with indicating the perfect forecast. TS is expressed as TS (number of yes forecasts in a particular category)/(total number of forecasts and observations in the same category). ) FOUR-CATEGORY FORECASTS In the case of polychotomous or multicategory rainfall forecasts, the Heidke skill score (HSS) and Hanssen and Kuipers score (HKS) were computed (Wilks ; Woodcock ). The general expression for the skill score (Wilks ), which is interpreted as a percentage improvement over the reference forecasts (ref), is written as A A SS ref ref %, A A perf where A perf is the value of the accuracy measure that would be achieved by perfect forecasts. The value A is the particular level of accuracy under investigation (here, the model forecasts). The value A ref is the accuracy for a reference set of forecasts such as persistence, climatological means, random values, and so on. In general, all skill scores above indicate improvement over the reference forecasts. A perfect forecast always corresponds to a skill of, and skill represents a forecast no better than the reference forecasts. Forecasts also may have negative skill scores if they perform more poorly than the reference forecast. (i) The HSS The HSS is a particular example of the generic skill score (Wilks ) in which accuracy is measured by HR. Thus the HSS can be interpreted as the HR, corrected for the number of correct forecasts that would be expected by some standard of comparison, generally defined as chance or random forecasts. It is defined as HSS [correct forecasts (correct forecasts) random ]/[N (correct forecasts) random ], where N is the total number of forecasts or maximum number of correct forecasts that can be achieved. HSS ranges from to, similar to the generic skill score. (ii) The HKS Known also as true skill statistic, the HKS was advocated by Woodcock () for measurement of forecast skill. HKS is formulated similar to the HSS except random forecasts that are constrained to be unbiased are used as the reference hit rate in the denominator. That is, it is assumed that the random reference forecasts in the denominator have a marginal distribution that is equal to that of the sample climatologic data. The HKS can be expressed as HKS [correct forecasts (correct forecasts) random ]/[N (correct forecasts) unbiasedrandom ]. Appropriate equations for all the verification measures presented above were adapted from Wilks () for computation of skill scores of forecasts presented in this study.. Results The time and space variation of weekly cumulative rainfall forecast errors of the T GCM over different meteorological subdivisions of India during the southwest monsoon season remained more or less similar during the -yr period from through. An ref
WEATHER AND FORECASTING VOLUME overview of the overall performance of the rainfall forecasts during this period is provided in Table. The Table includes the distribution by year of the number of subdivisions in which the rainfall accuracy falls under different categories. In the category column, the values,,, and indicate the forecast accuracy in terms of category deviations from observations, and negative values indicate underprediction. The table reveals that, in general, predictability of weekly cumulative rainfall is better for the weeks falling in the month of June, followed by those for July, September, and August. The fairly good prediction capability of the model in June, which is the onset phase of the southwest monsoon, can be fruitfully utilized for the prediction of rains for sowing different crops cultivated in different meteorological subdivisions of India. Rainfall predictions in the month of September also assume importance because the withdrawal phase of the monsoon, which also denotes the end of the seasonal rainfall period, falls in this month. Planning crop harvest operations to coincide with the dry period in this month is of paramount importance for quality of farm produce. To discuss the performance of these forecasts in respect to individual subdivisions, a sample of monsoon-season results are provided in Table. The deviations of the category of forecast rainfall (departure from normal) from the observed rainfall category (also departure from normal), in terms of the number of categories, are shown in the table for all subdivisions and weeks of the southwest monsoon season of. The positive numbers in the table indicate that the forecast category is for higher rainfall than the observed rainfall category, and negative numbers indicate the reverse. It can be inferred from Table that the subdivisions falling in and flanking the central Indian belt Orissa, plains of west Uttar Pradesh, Gujarat region, Saurashtra, coastal Andhra Pradesh, coastal Karnataka, north interior Karnataka have shown a better percentage of success than other subdivisions (see Fig. ). It suggests that the skill of the model to provide quantitative precipitation forecast for a week at subdivisional scale is good in this belt; hence, it is feasible to use the forecast in planning rain-dependent activities. In general, the subdivisions falling over and around the Western Ghats (an exception is coastal Karnataka), and the Himalayan foothills were not forecast well by the model. Kerala, Tamil Nadu, Karnataka, Goa, and Maharashtra are the states of India located over and around the Western Ghats. Probable reasons for the poor performance of the model in these areas are discussed later. Several factors influence the performance of a GCM rainfall forecast over a region. They include the parameterization of land surface processes, including soil moisture, vegetation type, and soil characteristics; representation of orography; and model resolution (Boer and Lazare ; Boyle ; Phillips et al. ). Inadequate parameterization of physical processes in the model can result in less accurate rainfall forecasts. The resolution of the T model, for instance, may not allow it to see the islands in the Bay of Bengal and the Arabian Sea. In a similar way, some of the coastal land area of the country may be treated as water by the model. The model orography also has some inaccuracies the steep mountains of the Himalayan region and Western Ghats are not accurately resolved by the current resolution of the model because their peaks are severely underestimated. In a similar way, proper representation of land surface processes in a GCM is always difficult if the resolution of the model is coarse. Coarse global models cannot resolve the rainfall events produced by mesoscale systems. Thus, the accuracy of forecasts using such models depends upon the size of the subdivisions. Figure shows the bias score of the weekly cumulative rainfall forecast over different subdivisions of India during the combined weeks of the monsoon seasons from to. A list of the names of different meteorological subdivisions of India is given in Table. The number shown in each subdivision in Fig. corresponds to the number given to the subdivision in Table. The bias score measures the correspondence between the average forecast and the average observed value of the predictand. This is different from accuracy, which measures the average correspondence between individual pairs of forecasts and observations (Hughes ; Anthes ; Wilks ). In the figure, a subdivision is marked as having no bias when the score calculated was equal to. When the score was less than, the subdivision was categorized as underforecast, and scores of more than were indicated as overforecast. It can be seen from Fig. that for the subdivisions that are on the windward side of the Western Ghats for the southwest monsoon winds, namely, Kerala (); Konkan and Goa (); coastal Karnataka (); and Gujarat region, Daman, Dadra, and Nagar Haveli (), the rainfall forecast by the model had negative bias (underforecast). Negative bias in forecast also was observed at the sub-himalayan subdivisions, namely, Himachal Pradesh (), hills of west Uttar Pradesh (), east Uttar Pradesh (), Bihar plains (), Punjab (), and west Rajasthan (). One of the reasons for these results can be the poor orography representation and the coarse model resolution of the T model. Importance of model resolution on forecast performance of GCMs was reported (Boer and Lazare ; Boyle ; Phillips et al. ). Negative biases in forecasts were also exhibited by the subdivisions Haryana, Chandigarh, and Delhi () and east Rajasthan (), which are located over the Indo- Gangetic plains and are less influenced by orographic modifications of rainfall. Positive bias in model forecast rainfall was exhibited for the subdivisions falling in the rain-shadow areas of Western Ghats, namely, Tamil Nadu and Pondicherry (); south interior Karnataka (); north interior Karnataka (); Madhya Maharashtra (); Vidarbha (); and Marathawada (). These results, too, point toward the poor resolution of orography in the model. Subdi-
AUGUST SASEENDRAN ET AL. TABLE. Distribution by year of the number of subdivisions in which rainfall accuracy falls into different categories. In the category column, the values,,, and indicate the forecast accuracy in terms of category deviation from observations, and negative values indicate underprediction. Weeks Category Jun Jul Aug Sep Total (all years)
WEATHER AND FORECASTING VOLUME TABLE. Performance of weekly cumulative subdivisional rainfall forecasts. Weeks during monsoon (Jun Sep) Perfect matching weeks Subdivision No. % No. of categories of deviation of forecast from observed* Andaman and Nicobar Arunachal Pradesh Assam and Meghalaya Nagaland, Manipur, Mizoram, and Tripura Sub-Himalayan West Bengal Gangetic West Bengal Orissa Bihar plateau Bihar plains East Uttar Pradesh Plains of west Uttar Pradesh Hills of west Uttar Pradesh Haryana, Chandigarh, and Delhi Punjab Himachal Pradesh Jammu and Kashmir West Rajasthan East Rajasthan West Madhya Pradesh East Madhya Pradesh Gujarat region, Daman, Dadra, and Nagar Haveli Saurashtra, Kutch, and Diu Konkan and Goa Madhya Maharashtra Marathawada Vidarbha Coastal Andhra Pradesh Telangana Rayalaseema Tamil Nadu and Pondicherry Coastal Karnataka North interior Karnataka South interior Karnataka Kerala Lakshadweep * Positive values indicate overprediction and negative values indicate underprediction; the numerals indicate the number of categories by which forecasts deviate from observed. Zero is for perfectly matching cases.
AUGUST SASEENDRAN ET AL. TABLE. Success (%) in model prediction of cyclogenesis of monsoon depressions during (Source: Akhilesh and Ranjeet ). Year No. of cyclogeneses predicted (%) h h h............. FIG.. Number of weeks having the forecast category perfectly matching the observed category over the different subdivisions (%). visions east Madhya Pradesh (), Bihar plateau (), and coastal Andhra Pradesh () also show positive bias in rainfall forecast; these are the subdivisions falling along the normal tracks of movement of the low pressure systems forming in the Bay of Bengal during the southwest monsoon season. This reflects on the overprediction of rainfall yield of monsoon depressions/lows by the model (Ramesh and Iyengar ). Nevertheless, the subdivisions of Orissa and Gangetic West Bengal also falling in the normal path of the bay systems did not show any bias in the forecasts, and further studies are required to provide an explanation for this. To provide a brief idea of the capability of the T model in predicting the cyclogeneses of monsoon depressions, statistics for the period are provided in Table. The table shows that the rate of prediction success of cyclogenesis of the monsoon systems decreases at a very fast rate as the forecast lead time increases from to h. Success rate varied from.% to.% in -h forecasts, from.% to % in -h forecasts, and from.% to.% in -h forecasts. Figure gives the hit rate computed over different subdivisions. The results indicate that the hit rate varied from. for the Tamil Nadu subdivision to for Na- FIG.. Subdivision bias scores of weekly cumulative rainfall forecasts. The subdivision numbers are identified in Table. FIG.. Distribution of hit-rate scores of weekly rainfall forecasts over the different meteorological subdivisions.
WEATHER AND FORECASTING VOLUME FIG.. Threat scores of weekly rainfall forecasts over different meteorological subdivisions for excess, normal, deficient, and scanty categories.
AUGUST SASEENDRAN ET AL. FIG.. Distribution of Hanssen and Kuipers scores of weekly rainfall forecasts over the different meteorological subdivisions. galand. Twenty-eight subdivisions have scores above.. Subdivision threat scores in the categories of excess, normal, deficient, and scanty rainfall forecasts calculated for the weeks are shown in Fig.. This figure shows that the TS scores for different forecast categories over the various subdivisions were between and.. Figure also shows that the rainfall in the excess and scanty categories of the forecast have better skill when compared with those in the normal and deficient categories; nonetheless, none of the subdivisions exhibited a TS value of, or a perfect forecast. The HKS score distribution over the different subdivisions of the country is given in Fig.. Three subdivisions, namely, Konkan and Goa (), Tamil Nadu (), and hills of west Uttar Pradesh (), have shown negative HKS values of.,., and. respectively. Twelve subdivisions along the central belt of the country and three over eastern and northeastern regions of India (see Fig. ) have shown HKS scores above.. HSS scores over different subdivisions are furnished in Fig.. Negative HSS values were scored by the same subdivisions that scored negative HKS values with identical magnitudes. Subdivisions that had HSS values of more than. also match well with those subdivisions that scored similar values under HKS scores, with the exception of Arunachal Pradesh () and Nagaland () in the northeastern region of India. POD and FAR values computed for different categories of weekly rainfall forecast (see Table for categories of rainfall forecasts) over different subdivisions FIG.. Distribution of Heidke skill scores of weekly rainfall forecasts over the different meteorological subdivisions. of India are provided in Figs. and, respectively. The POD and FAR scores for different categories of forecasts indicate that the weekly cumulative rainfall forecasts in the excess and scanty categories were better forecast than the normal and deficient categories, that is, the magnitude of the POD scores for the excess and scanty categories are higher than those for the normal and deficient categories, and FAR values for the excess and scanty categories are lower than those for the normal and deficient categories of forecasts. In the excess category, west Madhya Pradesh, Madhya Maharashtra, and Telengana have shown higher POD and lower FAR. In the normal category, all subdivisions have shown high FAR scores. Vidarbha in the deficient category and east Rajasthan, west Rajasthan, Gujarat Region, west Madhya Pradesh, plains of west Uttar Pradesh, east Uttar Pradesh, and Arunachal Pradesh in the scanty category have scored higher PODs and lower FARs. The observed and forecast all-india weekly rainfall is presented in Fig.. The figure shows that, on the all-india scale, the weekly rainfall activity is predicted reasonably well by the model in terms of the trend and fluctuations, though there are differences in the quantity.. Summary In general, the subdivisions falling in and flanking the central Indian belt consisting of Orissa, plains of west Uttar Pradesh, Gujarat region, Saurashtra, coastal Andhra Pradesh, coastal Karnataka, and north interior Karnataka showed a better percentage of success in
WEATHER AND FORECASTING VOLUME FIG.. Distribution of POD scores of weekly rainfall forecasts over the different meteorological subdivisions for different forecast categories. weekly cumulative rainfall forecasts. The subdivisions falling over and around the Western Ghats (an exception is coastal Karnataka) and the Himalayan foothills were not forecast well by the model. Bias scores revealed that the subdivisions that are on the windward side of the Western Ghats for the southwest monsoon winds, namely, Kerala; Konkan and Goa; coastal Karnataka; Gujarat region, Daman, Dadra, and Nagar Haveli, and Himalayan subdivisions, namely, Himachal Pradesh, hills of west Uttar Pradesh, east Uttar Pradesh, Bihar plains, Punjab, and west Rajasthan, were underforecast. Subdivisions, namely, Tamil Nadu and Pondicherry, south interior Karnataka, north interior Karnataka, Madhya Maharashtra, Vidarbha, and Marathawada, that fall in the rain-shadow areas of the Western Ghats were overforecast. Hit-rate scores varied from. for the Tamil Nadu subdivision to for Nagaland. Twenty-eight subdivisions had scores above.. The TS scores for different categories of forecasts over different subdivisions were between and.. In general, the rainfall in the excess and scanty categories of forecast had better skill when compared with those in the normal and deficient categories. Three subdivisions, namely, Konkan and Goa, Tamil Nadu, and hills of west Uttar Pradesh, had
AUGUST SASEENDRAN ET AL. FIG.. Distribution of FAR scores of weekly rainfall forecasts over the different meteorological subdivisions for different forecast categories. negative HKS values of.,., and., respectively. Twelve subdivisions along the central belt of the country and three over eastern and northeastern regions of India had HKS scores above.. Negative HSS values were scored by the same subdivisions that scored negative HKS values with identical magnitudes. Subdivisions that had HSS values of more than. also match well with those subdivisions that scored similar values under HKS scores, with the exception of Arunachal Pradesh and Nagaland in the northeastern
WEATHER AND FORECASTING VOLUME FIG.. Comparison of observed and predicted all-india-scale weekly rainfall from to. region of India. POD and FAR computations revealed that forecasts in the excess and scanty categories were better than those in the normal and deficient categories. The fairly good prediction capability of the model in the month of June, which includes the onset and advance phase of the monsoon, can be fruitfully utilized for the prediction of rains for sowing different crops and for various other rainfall-related operations. Also, the model is able to predict the weekly monsoon rainfall on an all-india scale, which is promising for large-scale planning of rainfall-related activities in the country as a whole. REFERENCES Akhilesh, G., and S. Ranjeet, : Genesis and movement of lowpressure systems over Indian region. Evolution and Maintenance of the Summer Monsoon, R. K. Paliwal, Z. N. Begum, K. J. Ramesh, and G. Akhilesh, Eds., Global Data Assimilation Forecast System of India,. [Available from Dr. Akhilesh Gupta, NCMRWF, Mausam Bhavan, Lodi Road, New Delhi-, India] Anthes, R. A., : Regional models of the atmosphere in middle latitudes. Mon. Wea. Rev.,,. Boer, G. J., and M. Lazare, : Some results concerning the effect of horizontal resolution and gravity wave drag on simulated climate. J. Climate,,. Boyle, J. S., : Sensitivity of dynamical quantities to horizontal resolution for a climate simulation using the ECMWF (Cycle ) model. J. Climate,,. Doswell, C. A., III, R. Davies-Jones, and D. L. Keller, : On summary measures of skill in rare event forecasting based on contingency tables. Wea. Forecasting,,. Hughes, L. A., : Precipitation probability forecasts problems seen via a comprehensive verification. Mon. Wea. Rev.,,. Murphy, A. H., : The Finley affair: A signal event in the history of forecast verification. Wea. Forecasting,,., and R. L. Winkler, : A general framework for forecast verification. Mon. Wea. Rev.,,., B. G. Brown, and Y. S. Chen, : Diagnostic verification of temperature forecasts. Wea. Forecasting,,. Philips, T. J., L. C. Corsetti, and S. L. Grotch, : The impact of horizontal resolution on moist processes in the ECMWF model. Climate Dyn.,,. Ramesh, K. J., and G. R. Iyengar, : Characteristics of medium range rainfall forecasts of the summer monsoon. Int. J. Climatol.,,. Saseendran, S. A., L. S. Rathore, P. S. Rao, and S. Das, : Performance of weekly cumulative rainfall forecasts of T model during monsoon. Advanced Technologies in Meteorology, R. K. Gupta and T. S. Viswanadham, Eds., Tata McGraw-Hill,. Schaefer, J. T., : The critical success index as an indicator of warning skill. Wea. Forecasting,,. Sela, J. G., : Spectral modeling at the National Meteorological Center. Mon. Wea. Rev.,,. Woodcock, F., : The evaluation of yes/no forecasts for scientific and administrative purposes. Mon. Wea. Rev.,,., : Hanssen and Kuipers discriminant related to the utility of yes/no forecasts. Mon. Wea. Rev.,,. Wilks, D. S., : Statistical Methods in Atmospheric Sciences. Academic Press, pp.