Weather forecasting is crucial to both the demand and supply sides of electricity systems. Temperature has a great effect on the demand side. Moreover, solar and wind are very promising renewable energy sources and ar...Weather forecasting is crucial to both the demand and supply sides of electricity systems. Temperature has a great effect on the demand side. Moreover, solar and wind are very promising renewable energy sources and are, thus, important on the supply side. In this paper, a large vector autoregression(VAR) model is built to forecast three important weather variables for 61 cities around the United States. The three variables at all locations are modeled as response variables. Lag terms are used to capture the relationship between observations in adjacent periods and daily and annual seasonality are modeled to consider the correlation between the same periods in adjacent days and years. We estimate the VAR model with16 years of hourly historical data and use two additional years of data for out-of-sample validation. Forecasts of up to six-hours-ahead are generated with good forecasting performance based on mean absolute error, root mean square error, relative root mean square error, and skill scores. Our VAR model gives forecasts with skill scoresthat are more than double the skill scores of other forecasting models in the literature. Our model also provides forecasts that outperform persistence forecasts by between6% and 80% in terms of mean absolute error. Our results show that the proposed time series approach is appropriate for very short-term forecasting of hourly solar radiation,temperature, and wind speed.展开更多
Tropical cyclones (TCs) and storms (TSs) are among the devastating events in the world and southwestern Indian Ocean (SWIO) in particular. The seasonal forecasting TCs and TSs for December to March (DJFM) and November...Tropical cyclones (TCs) and storms (TSs) are among the devastating events in the world and southwestern Indian Ocean (SWIO) in particular. The seasonal forecasting TCs and TSs for December to March (DJFM) and November to May (NM) over SWIO were conducted. Dynamic parameters including vertical wind shear, mean zonal steering wind and vorticity at 850 mb were derived from NOAA (NCEP-NCAR) reanalysis 1 wind fields. Thermodynamic parameters including monthly and daily mean Sea Surface Temperature (SST), Outgoing Longwave Radiation (OLR) and equatorial Standard Oscillation Index (SOI) were used. Three types of Poison regression models (i.e. dynamic, thermodynamic and combined models) were developed and validated using the Leave One Out Cross Validation (LOOCV). Moreover, 2 × 2 square matrix contingency tables for model verification were used. The results revealed that, the observed and cross validated DJFM and NM TCs and TSs strongly correlated with each other (p ≤ 0.02) for all model types, with correlations (r) ranging from 0.62 - 0.86 for TCs and 0.52 - 0.87 for TSs, indicating great association between these variables. Assessment of the model skill for all model types of DJFM and NM TCs and TSs frequency revealed high skill scores ranging from 38% - 70% for TCs and 26% - 72% for TSs frequency, respectively. Moreover, results indicated that the dynamic and combined models had higher skill scores than the thermodynamic models. The DJFM and NM selected predictors explained the TCs and TSs variability by the range of 0.45 - 0.65 and 0.37 - 0.66, respectively. However, verification analysis revealed that all models were adequate for predicting the seasonal TCs and TSs, with high bias values ranging from 0.85 - 0.94. Conclusively, the study calls for more studies in TCs and TSs frequency and strengths for enhancing the performance of the March to May (MAM) and December to October (OND) seasonal rainfalls in the East African (EA) and Tanzania in particular.展开更多
The Tibetan Plateau, with an average altitude above 4000 m, is the highest and largest plateau in the world. The frequency of thunderstorms in this region is extremely high. Many indices are used in operational foreca...The Tibetan Plateau, with an average altitude above 4000 m, is the highest and largest plateau in the world. The frequency of thunderstorms in this region is extremely high. Many indices are used in operational forecasting to assess the stability of the atmosphere and predict the probability of severe thunderstorm development. One of the disadvantages of many of these indices is that they are mainly based on observations from plains. However, considering the Plateau＇s high elevation, most convective parameters cannot be applied directly, or their application is ineffective. The pre-convective environment on thunderstorm days in this region is investigated based on sounding data obtained throughout a five-year period（2006–10）.Thunderstorms occur over the Tibetan Plateau under conditions that differ strikingly from those in plains. On this basis,stability indices, such as the Showalter index（including SI and SICCL）, and the K index are improved to better assess the thunderstorm environments on the Plateau. Verification parameters, such as the true-skill statistic（TSS） and Heidke skill score（HSS）, are adopted to evaluate the optimal thresholds and relative forecast skill for each modified index. Lastly, the modified indices are verified with a two-year independent dataset（2011–12）, showing satisfactory results for the modified indices. For determining whether or not a thunderstorm day is likely to occur, we recommend the modified SICCLindex.展开更多
Statistical methods for category（yes/no） forecasts, such as the Threat Score, are typically used in the verification of precipitation forecasts. However, these standard methods are affected by the so-called ＂double...Statistical methods for category（yes/no） forecasts, such as the Threat Score, are typically used in the verification of precipitation forecasts. However, these standard methods are affected by the so-called ＂double-penalty＂ problem caused by slight displacements in either space or time with respect to the observations. Spatial techniques have recently been developed to help solve this problem. The fractions skill score（FSS）, a neighborhood spatial verification method, directly compares the fractional coverage of events in windows surrounding the observations and forecasts.We applied the FSS to hourly precipitation verification by taking hourly forecast products from the GRAPES（Global/Regional Assimilation Prediction System） regional model and quantitative precipitation estimation products from the National Meteorological Information Center of China during July and August 2016, and investigated the difference between these results and those obtained with the traditional category score. We found that the model spin-up period affected the assessment of stability. Systematic errors had an insignificant role in the fraction Brier score and could be ignored. The dispersion of observations followed a diurnal cycle and the standard deviation of the forecast had a similar pattern to the reference maximum of the fraction Brier score. The coefficient of the forecasts and the observations is similar to the FSS; that is, the FSS may be a useful index that can be used to indicate correlation.Compared with the traditional skill score, the FSS has obvious advantages in distinguishing differences in precipitation time series, especially in the assessment of heavy rainfall.展开更多
基金supported by the National Science Foundation (No: 1029337)supported by an allocation of computing time from the Ohio Supercomputer Center
文摘Weather forecasting is crucial to both the demand and supply sides of electricity systems. Temperature has a great effect on the demand side. Moreover, solar and wind are very promising renewable energy sources and are, thus, important on the supply side. In this paper, a large vector autoregression(VAR) model is built to forecast three important weather variables for 61 cities around the United States. The three variables at all locations are modeled as response variables. Lag terms are used to capture the relationship between observations in adjacent periods and daily and annual seasonality are modeled to consider the correlation between the same periods in adjacent days and years. We estimate the VAR model with16 years of hourly historical data and use two additional years of data for out-of-sample validation. Forecasts of up to six-hours-ahead are generated with good forecasting performance based on mean absolute error, root mean square error, relative root mean square error, and skill scores. Our VAR model gives forecasts with skill scoresthat are more than double the skill scores of other forecasting models in the literature. Our model also provides forecasts that outperform persistence forecasts by between6% and 80% in terms of mean absolute error. Our results show that the proposed time series approach is appropriate for very short-term forecasting of hourly solar radiation,temperature, and wind speed.
文摘Tropical cyclones (TCs) and storms (TSs) are among the devastating events in the world and southwestern Indian Ocean (SWIO) in particular. The seasonal forecasting TCs and TSs for December to March (DJFM) and November to May (NM) over SWIO were conducted. Dynamic parameters including vertical wind shear, mean zonal steering wind and vorticity at 850 mb were derived from NOAA (NCEP-NCAR) reanalysis 1 wind fields. Thermodynamic parameters including monthly and daily mean Sea Surface Temperature (SST), Outgoing Longwave Radiation (OLR) and equatorial Standard Oscillation Index (SOI) were used. Three types of Poison regression models (i.e. dynamic, thermodynamic and combined models) were developed and validated using the Leave One Out Cross Validation (LOOCV). Moreover, 2 × 2 square matrix contingency tables for model verification were used. The results revealed that, the observed and cross validated DJFM and NM TCs and TSs strongly correlated with each other (p ≤ 0.02) for all model types, with correlations (r) ranging from 0.62 - 0.86 for TCs and 0.52 - 0.87 for TSs, indicating great association between these variables. Assessment of the model skill for all model types of DJFM and NM TCs and TSs frequency revealed high skill scores ranging from 38% - 70% for TCs and 26% - 72% for TSs frequency, respectively. Moreover, results indicated that the dynamic and combined models had higher skill scores than the thermodynamic models. The DJFM and NM selected predictors explained the TCs and TSs variability by the range of 0.45 - 0.65 and 0.37 - 0.66, respectively. However, verification analysis revealed that all models were adequate for predicting the seasonal TCs and TSs, with high bias values ranging from 0.85 - 0.94. Conclusively, the study calls for more studies in TCs and TSs frequency and strengths for enhancing the performance of the March to May (MAM) and December to October (OND) seasonal rainfalls in the East African (EA) and Tanzania in particular.
基金supported by the National Natural Science Foundation of China (Grant Nos. 41275128, 41375063 and 41206163)the Chengdu Institute of Plateau Meteorology Foundation
文摘The Tibetan Plateau, with an average altitude above 4000 m, is the highest and largest plateau in the world. The frequency of thunderstorms in this region is extremely high. Many indices are used in operational forecasting to assess the stability of the atmosphere and predict the probability of severe thunderstorm development. One of the disadvantages of many of these indices is that they are mainly based on observations from plains. However, considering the Plateau＇s high elevation, most convective parameters cannot be applied directly, or their application is ineffective. The pre-convective environment on thunderstorm days in this region is investigated based on sounding data obtained throughout a five-year period（2006–10）.Thunderstorms occur over the Tibetan Plateau under conditions that differ strikingly from those in plains. On this basis,stability indices, such as the Showalter index（including SI and SICCL）, and the K index are improved to better assess the thunderstorm environments on the Plateau. Verification parameters, such as the true-skill statistic（TSS） and Heidke skill score（HSS）, are adopted to evaluate the optimal thresholds and relative forecast skill for each modified index. Lastly, the modified indices are verified with a two-year independent dataset（2011–12）, showing satisfactory results for the modified indices. For determining whether or not a thunderstorm day is likely to occur, we recommend the modified SICCLindex.
基金Supported by the National Key Research and Development Program(2017YFA0604500)China Meteorological Administration Special Public Welfare Research Fund(GYHY201506002)+1 种基金China Meteorological Administration Special Project for Forecasters(YBGJXM(2017)06)National Natural Science Foundation of China(41305091)
文摘Statistical methods for category（yes/no） forecasts, such as the Threat Score, are typically used in the verification of precipitation forecasts. However, these standard methods are affected by the so-called ＂double-penalty＂ problem caused by slight displacements in either space or time with respect to the observations. Spatial techniques have recently been developed to help solve this problem. The fractions skill score（FSS）, a neighborhood spatial verification method, directly compares the fractional coverage of events in windows surrounding the observations and forecasts.We applied the FSS to hourly precipitation verification by taking hourly forecast products from the GRAPES（Global/Regional Assimilation Prediction System） regional model and quantitative precipitation estimation products from the National Meteorological Information Center of China during July and August 2016, and investigated the difference between these results and those obtained with the traditional category score. We found that the model spin-up period affected the assessment of stability. Systematic errors had an insignificant role in the fraction Brier score and could be ignored. The dispersion of observations followed a diurnal cycle and the standard deviation of the forecast had a similar pattern to the reference maximum of the fraction Brier score. The coefficient of the forecasts and the observations is similar to the FSS; that is, the FSS may be a useful index that can be used to indicate correlation.Compared with the traditional skill score, the FSS has obvious advantages in distinguishing differences in precipitation time series, especially in the assessment of heavy rainfall.
