A Comparative Study of Weather Forecasting Simulation Models through Sensors for Accurate Monsoon Predictions in the Indian Ocean

When Monsoon depressions form over the seas, the Moderate Resolution Imaging Spectroradiometer (MODIS) provides humidity and high-horizontal resolution temperature details about the depressions. These high-resolution satellite data related to temperature and humidity can improve the poor predicting rate of depressions [1]. Using three-dimensional variational data assimilation (3DVAR) and with the help of humidity profiles along with MODIS temperature. We can achieve an advanced prospect of detection and a larger value of (ETS) equitable threat score observed over 48 hours collected precipitation with respect to the control run. The 3DVAR assimilation of Doppler Weather Radar wind data associated with Indian Meteorological Department (IMD) surface data and upper air data helped in the improvements in the simulation of strong gradients associated with horizontal wind speed ,higher warm core temperature , high vertical velocity & better precipitation and spatial distribution.[2]. The effect of Spectral sensor microwave imager (SSM/I), humidity profiles, use of Advanced TIROS Vertical Sounder (ATOVS) temperature and total precipitable water (TPW) helped in improving the ‘‘forecast impact’’ parameters of ‘‘bias score’’ and ‘‘equitable threat score’’ with respect to the assimilation of satellite observation[3] . In this paper we have discussed a comparative study of different proposed techniques to analyze its effects in improving the low prediction rates of depressions.

On the Calculation and Correction of Equitable Threat Score for Model Quantitative Precipitation Forecasts for Small Verification Areas: The Example of Taiwan

As one of the most widely used skill scores for model quantitative precipitation forecast (QPF) verification and evaluation, the equitable threat score ETS differs from the threat score TS in that the random hits R are removed from its calculation. In practice, however, when applied to a set of verification points confined to small areas, the random assumption often becomes increasingly questionable or even invalid in larger events. As a result, the random hits are overestimated and the ETS becomes biased and not indicative of model skills. In this paper, such an issue is explored and demonstrated through the example of Taiwan with steep topography from Typhoon Morakot (2009) and mei-yu heavy-rainfall cases. It is found that the ETS is affected more seriously and scaled down by at least about 0.1 compared to the TS whenever the rain area occupies roughly 20% or more of the total verification area (if the random assumption of R is invalid). As such conditions often occur for small areas, it is vital to estimate R as correctly as possible for the ETS to work properly. A simple solution is offered by using all gridpoint values from the entire model domain, rather than just a small subset falling into the verification area, to estimate the random hit rate in the forecast. While the ETS remains unaltered in its definition, the proposed method yields the best estimates of R available by using the largest sample size from the model and subsequently better-behaved ETS values and is, therefore, recommended for all applications of ETS for small verification areas.

On an Enhanced PERSIANN-CCS Algorithm for Precipitation Estimation

By employing wavelet and selected features (WSF), median merging (MM), and selected curve-fitting (SCF) techniques, the Precipitation Estimation from Remotely Sensed Imagery using an Artificial Neural Networks Cloud Classification System (PERSIANN-CCS) has been improved. The PERSIANN-CCS methodology includes the following four main steps: 1) segmentation of satellite cloud images into cloud patches, 2) feature extraction, 3) classification of cloud patches, and 4) derivation of the temperature–rain-rate (T–R) relationship for every cluster. The enhancements help improve step 2 by employing WSF, and step 4 by employing MM and SCF. For the study area herein, the results show that the enhanced methodology improves the equitable threat score (ETS) of the daily and hourly rainfall estimates mostly in the winter and fall. The ETS percentage improvement is about 20% for the daily (10% for hourly) estimates in the winter, 10% for the daily (8% for hourly) estimates in the fall, and at most 5% for the daily estimates in the summer at some rainfall thresholds. In the winter and fall, the area bias is improved almost at all rainfall thresholds for daily and hourly estimates. However, no significant improvement is obtained in the spring, and the area bias in the summer is also greater than that of the implemented PERSIANN-CCS algorithm.

The Response of Performance Metrics for Binary Forecasts to Hedging that Approaches Random Change

Critical performance ratio (CPR) expressions for the eight conditional probabilities associated with the 2 × 2 contingency table of outcomes for binary (dichotomous “yes” or “no”) forecasts are derived. Two are shown to be useful in evaluating the effects of hedging as it approaches random change. The CPR quantifies how the probability of detection (POD) must change as frequency bias changes, so that a performance measure (or conditional probability) indicates an improved forecast for a given value of frequency bias. If yes forecasts were to be increased randomly, the probability of additional correct forecasts (hits) is given by the detection failure ratio (DFR). If the DFR for a performance measure is greater than the CPR, the forecast is likely to be improved by the random increase in yes forecasts. Thus, the DFR provides a benchmark for the CPR in the case of frequency bias inflation. If yes forecasts are decreased randomly, the probability of removing a hit is given by the frequency of hits (FOH). If the FOH for a performance measure is less than the CPR, the forecast is likely to be improved by the random decrease in yes forecasts. Therefore, the FOH serves as a benchmark for the CPR if the frequency bias is decreased. The closer the FOH (DFR) is to being less (greater) than or equal to the CPR, the more likely it may be to enhance the performance measure by decreasing (increasing) the frequency bias. It is shown that randomly increasing yes forecasts for a forecast that is itself better than a randomly generated forecast can improve the threat score but is not likely to improve the equitable threat score. The equitable threat score is recommended instead of the threat score whenever possible.

Equitability Revisited: Why the “Equitable Threat Score” Is Not Equitable

In the forecasting of binary events, verification measures that are “equitable” were defined by Gandin and Murphy to satisfy two requirements: 1) they award all random forecasting systems, including those that always issue the same forecast, the same expected score (typically zero), and 2) they are expressible as the linear weighted sum of the elements of the contingency table, where the weights are independent of the entries in the table, apart from the base rate. The authors demonstrate that the widely used “equitable threat score” (ETS), as well as numerous others, satisfies neither of these requirements and only satisfies the first requirement in the limit of an infinite sample size. Such measures are referred to as “asymptotically equitable.” In the case of ETS, the expected score of a random forecasting system is always positive and only falls below 0.01 when the number of samples is greater than around 30. Two other asymptotically equitable measures are the odds ratio skill score and the symmetric extreme dependency score, which are more strongly inequitable than ETS, particularly for rare events; for example, when the base rate is 2% and the sample size is 1000, random but unbiased forecasting systems yield an expected score of around −0.5, reducing in magnitude to −0.01 or smaller only for sample sizes exceeding 25 000. This presents a problem since these nonlinear measures have other desirable properties, in particular being reliable indicators of skill for rare events (provided that the sample size is large enough). A potential way to reconcile these properties with equitability is to recognize that Gandin and Murphy’s two requirements are independent, and the second can be safely discarded without losing the key advantages of equitability that are embodied in the first. This enables inequitable and asymptotically equitable measures to be scaled to make them equitable, while retaining their nonlinearity and other properties such as being reliable indicators of skill for rare events. It also opens up the possibility of designing new equitable verification measures.

Applying a General Analytic Method for Assessing Bias Sensitivity to Bias-Adjusted Threat and Equitable Threat Scores

Bias-adjusted threat and equitable threat scores were designed to account for the effects of placement errors in assessing the performance of under- or overbiased forecasts. These bias-adjusted performance measures exhibit bias sensitivity. The critical performance ratio (CPR) is the minimum fraction of added forecasts that are correct for a performance measure to indicate improvement if bias is increased. In the opposite case, the CPR is the maximum fraction of removed forecasts that are correct for a performance measure to indicate improvement if bias is decreased. The CPR is derived here for the bias-adjusted threat and equitable threat scores to quantify bias sensitivity relative to several other measures of performance including conventional threat and equitable threat scores. The CPR for a bias-adjusted equitable threat score may indicate the likelihood of preserving or increasing the conventional equitable threat score if forecasts are bias corrected based on past performance.

Bias Adjusted Precipitation Threat Scores

Among the wide variety of performance measures available for the assessment of skill of deterministic precipitation forecasts, the equitable threat score (ETS) might well be the one used most frequently. It is typically used in conjunction with the bias score. However, apart from its mathematical definition the meaning of the ETS is not clear. It has been pointed out (Mason, 1989; Hamill, 1999) that forecasts with a larger bias tend to have a higher ETS. Even so, the present author has not seen this having been accounted for in any of numerous papers that in recent years have used the ETS along with bias "as a measure of forecast accuracy". A method to adjust the threat score (TS) or the ETS so as to arrive at their values that correspond to unit bias in order to show the model's or forecaster's accuracy in \\textit{placing} precipitation has been proposed earlier by the present author (Mesinger and Brill, the so-called dH/dF method). A serious deficiency however has since been noted with the dH/dF method in that the hypothetical function that it arrives at to interpolate or extrapolate the observed value of hits to unit bias can have values of hits greater than forecast when the forecast area tends to zero. Another method is proposed here based on the assumption that the increase in hits per unit increase in false alarms is proportional to the yet unhit area. This new method removes the deficiency of the dH/dF method. Examples of its performance for 12 months of forecasts by three NCEP operational models are given.

Evaluation of two Eta Model versions for weather forecast over South America

En este artículo se compara el desempeño de dos versiones del modelo regional Eta utilizado en el CPTEC. La segunda variante, que es una actualización de la empleada en forma operativa hasta el momento, presenta un dominio de integración mayor, un tope más elevado e incluye una representación del suelo y la vegetación. El modelo de suelo/vegetación posee dos capas subterráneas y una canopia vegetal. La evaluación del modelo se llevó a cabo comparando los errores medios y cuadráticos medios de diversas variables sobre un conjunto de 15 situaciones meteorológicas. Esta comparación se realizó utilizando los análisis del NCEP y datos aerológicos y pluviométricos de algunas estaciones de América del Sur. Los pronósticos de precipitación fueron evaluados por medio del bias (BIAS) y el equitable threat score (ETS). Los errores medios no difieren notablemente para ambas variantes del modelo durante las primeras 24 horas de previsión, excepto por la temperatura de superficie que es pronosticada con mayor acierto por la versión actualizada. Sin embargo las diferencias se hacen mucho más notables para los pronósticos mayores de 48 horas, donde la nueva versión logra un grado de verificación mucho mayor para la temperatura y la humedad. Estas diferencias se amplían sobre las grandes áreas forestadas de la América del Sur subtropical. Los contrastes son menores para la altura geopotencial y prácticamente nulos para las componentes zonal y meridional del viento. Los pronósticos de precipitación mostraron que durante las primeras 24 horas la nueva versión del modelo produce ETS ligeramente más elevados y BIAS similares, pero que luego de 48 horas éste tiende a sobrestimar en mayor medida la precipitación, sin alterar su verificación espacial.