Minimum Temperature Forecasts Research Articles

PREDICTIVE MODEL TO MINIMIZE THE EFFECT OF EXTREME TEMPERATURE IN SKARDU AND ASTORE, GILGIT BALTISTAN

Climate is a fundamental factor of the natural environment that has a role in both natural and human existence. Temperature is an important climatic element that influences snow melting, evaporation, and frost directly. Current study has used Mean Monthly Minimum Temperature (MMMT) of Skardu from 1972 to 2021 and of Astore from 1993 to 2021 based on the availability of data. In this work; we have used SARIMA (Seasonal Auto Regressive Integrated Moving Average Model) to forecast mean monthly minimum temperature. Skardu data is stationary at level form, which suggests SARMA model for Skardu station and Astore data is stationary at first difference so SARIMA time series is appropriate for mean monthly minimum temperature of Astore. Using Box Jenkins’s approach it is found that the most appropriate model for Skardu is SARMA(1,0)(1,0)12 and Astore is SARIMA (0,1,1)(4,1,4)12 respectively. These models have been utilized to forecast MMMT from 2022 to 2036. Yearly mean minimum temperature forecasts show that the mean minimum temperature at Skardu and Astore stations is slightly decreasing. The yearly mean minimum temperature at Skardu station is 4.0 °C in 2022 and will decrease to 2.3 °C till 2036, while at Astore station it is 4.0 °C in 2022 and will fall to 2.6 °C in 2036. Our results will be useful for decision makers and insurance companies for better future planning to minimize the effect of lowest temperature.

A new statistical downscaling approach for short‐term forecasting of summer air temperatures through a fusion of deep learning and spatial interpolation

AbstractReliable early forecasting of extreme summer air temperatures is essential for effectively managing and mitigating the socioeconomic damage caused by thermal disasters. Numerical weather prediction models have become valuable tools for forecasting air temperature; however, they incur high computational costs, resulting in coarse spatial resolution and systematic bias owing to imperfect parametrization. To address these problems, we developed a novel statistical downscaling and bias correction method (named DeU‐Net) for the maximum and minimum air temperature (Tmax and Tmin respectively) forecasts obtained from the Global Data Assimilation and Prediction System with a spatial resolution of 10 to 1.5 km over South Korea through the fusion of deep learning (i.e., U‐Net) and spatial interpolation. In this study, we used a methodology to decompose statistically downscaled Tmax and Tmin forecasts into temporal dynamics over South Korea and spatial fluctuations by pixels. When comparing the proposed DeU‐Net with the dynamical downscaling model (i.e., Local Data Assimilation and Prediction System) and support vector regression‐based statistical downscaling model at the seen and unseen stations for forecasting the next‐day Tmax and Tmin, DeU‐Net showed the highest spatial correlation and the lowest root‐mean‐square error in all cases. In a qualitative evaluation, DeU‐Net successfully produced a detailed spatial distribution most similar to the observations. A further comparison extending the forecast lead time to 7 days indicated that the proposed DeU‐Net is a better downscaling approach than support vector regression, regardless of the forecast lead time. These results demonstrate that bias‐corrected high spatial resolution air temperature forecasts with relatively long forecast lead times in summer can be effectively produced using the proposed model for operational forecasting.

A Comparison of the Statistical Downscaling and Long-Short-Term-Memory Artificial Neural Network Models for Long-Term Temperature and Precipitations Forecasting

General circulation models (GCMs) run at regional resolution or at a continental scale. Therefore, these results cannot be used directly for local temperatures and precipitation prediction. Downscaling techniques are required to calibrate GCMs. Statistical downscaling models (SDSM) are the most widely used for bias correction of GCMs. However, few studies have compared SDSM with multi-layer perceptron artificial neural networks and in most of these studies, results indicate that SDSM outperform other approaches. This paper investigates an alternative architecture of neural networks, namely the long-short-term memory (LSTM), to forecast two critical climate variables, namely temperature and precipitation, with an application to five climate gauging stations in the Lake Chad Basin. Lake Chad is a data scarce area which has been impacted by severe drought, where water resources have been influenced by climate change and recent agricultural expansion. SDSM was used as the benchmark in this paper for temperature and precipitation downscaling for monthly time–scales weather prediction, using grid resolution GCM output at a 5 degrees latitude × 5 degrees longitude global grid. Three performance indicators were used in this study, namely the root mean square error (RMSE), to measure the sensitivity of the model to outliers, the mean absolute percentage error (MAPE), to estimate the overall performance of the predictions, as well as the Nash Sutcliffe Efficiency (NSE), which is a standard measure used in the field of climate forecasting. Results on the validation set for SDSM and test set for LSTM indicated that LSTM produced better accuracy on average compared to SDSM. For precipitation forecasting, the average RMSE and MAPE for LSTM were 33.21 mm and 24.82% respectively, while the average RMSE and MAPE for SDSM were 53.32 mm and 34.62% respectively. In terms of three year ahead minimum temperature forecasts, LSTM presents an average RMSE of 4.96 degree celsius and an average MAPE of 27.16%, while SDSM presents an average RMSE of 8.58 degree celsius and an average MAPE of 12.83%. For maximum temperatures forecast, LSTM presents an average RMSE of 4.27 degree celsius and an average MAPE of 11.09 percent, while SDSM presents an average RMSE of 9.93 degree celsius and an average RMSE of 12.07%. Given the results, LSTM may be a suitable alternative approach to downscale global climate simulation models’ output, to improve water management and long-term temperature and precipitations forecasting at local level.

Comparison between the Operational and Statistical Daily Maximum and Minimum Temperature Forecasts on the Central Coast of Peru

Abstract Multiple linear regression models were developed for 1–3-day lead forecasts of maximum and minimum temperature for two locations in the city of Lima, on the central coast of Peru (12°S), and contrasted with the operational forecasts issued by the National Meteorological and Hydrological Service—SENAMHI and the output of a regional numerical atmospheric model. We developed empirical models, fitted to data from the 2000–13 period, and verified their skill for the 2014–19 period. Since El Niño produces a strong low-frequency signal, the models focus on the high-frequency weather and subseasonal variability (60-day cutoff). The empirical models outperformed the operational forecasts and the numerical model. For instance, the high-frequency annual correlation coefficient and root-mean-square error (RMSE) for the 1-day lead forecasts were 0.37°–0.53° and 0.74°–1.76°C for the empirical model, respectively, but from around −0.05° to 0.24° and 0.88°–4.21°C in the operational case. Only three predictors were considered for the models, including persistence and large-scale atmospheric indices. Contrary to our belief, the model skill was lowest for the austral winter (June–August), when the extratropical influence is largest, suggesting an enhanced role of local effects. Including local specific humidity as a predictor for minimum temperature at the inland location substantially increased the skill and reduced its seasonality. There were cases in which both the operational and empirical forecast had similar strong errors and we suggest mesoscale circulations, such as the low-level cyclonic vortex over the ocean, as the culprit. Incorporating such information could be valuable for improving the forecasts. Significance Statement We wanted to compare the temperature of the operational forecast of the Meteorological and Hydrological Service, an atmospheric model, and persistence with the observed temperatures on the Peruvian central coast. In addition, we generated an empirical forecast model considering both atmospheric and local predictors. We got better results with this empirical model, considering the highest Pearson correlations and the lowest RMSE values. These results will allow us to use this empirical model as the main tool to automate the forecast on the central coast of Peru. Future work should be aimed at testing the skill of this model for forecasting in other cities of Peru.

鲁中地区冬季设施西瓜最低气温变化特征及预报模型研究

鲁中地区冬季设施西瓜最低气温变化特征及预报模型研究

Вплив категорій землекористування на прогноз WRF та його точність

In this research analysis of possibilities and methods of correction or reclassification of land use and land cover data, obtained modeling effects of using more accurate land use and land cover data and estimations of improvements of surface meteorological parameters simulations by mesoscale numerical atmospheric prediction (NWP) model. For the Kyiv region area, it is shown that the default WRF land use and land cover datasets are not ideal and contain various types of inaccuracies. Thus, compared to USGS, MODIS IGBP is characterized by a better spatial resolution, more correctly describing the urban built-up territories, forest areas and the Dnipro riverbed, but MODIS IGBP does not display small settlements. Analysis of other researches had shown that most of the reviewed studies are based on the case study approach, namely on an analysis of a small number of specific simulations. The disadvantage of this approach is that the sample size from which conclusions can be drawn is insignificant, so it is useful to compare similar studies for the consistency of the obtained results. The most consistent conclusions regarding the simulation improvement by the land use and land cover modification in the WRF model are related to the parameters of air temperature and humidity. Thus, the usage of more accurate and up-to-date land use and land cover databases in WRF simulations made it possible to reduce bias of forecasts and improved the accuracy of minimum and maximum daily temperature forecasts. The most significant improvement in the performed forecasts concerns the night temperature. The second parameter that were most often able to predict better is relative humidity. Improvement of simulated wind parameters using another land use and land cover dataset in the WRF model may be possible in urban areas and near the coast, especially as improving description of breeze circulation by the model. Assessments regarding the possibility of improving WRF precipitation simulation by land use and land cover dataset modification are not definite, although some works have noted the influence of the heat island of urbanized areas on redistribution of precipitation. On the one hand, in most of the analyzed scientific works, land use and land cover changes did not allow to significantly improve precipitation forecasts using the WRF model. On the other hand, it can be assumed that the physical and geographical conditions of a simulation domain as well as available moisture in the atmosphere is playing a more significant role in precipitation modeling than land use and land cover.

Exploratory Data Analysis of Accuracy of US Weather Forecastes

How reliable are the weather forecasts? Based on data collected from onehundred and thirteen cities in the United States over 38 months on threevariables, maximum temperature, minimum temperature and precipitation,accuracy of the weather forecasts was examined. The same day forecast hasbeen extremely accurate, especially for the maximum temperature, whilethe forecast errors and variability increase as forecasts go further out indays. Some cities have larger or smaller forecast errors than the others.For long-term weather forecasts, the maximum and minimum temperatureforecast errors has decreasing correlations overtime, respectively; However,the correlation between maximum and minimum temperature forecast errorsis positive and increasing overtime. The 7-days forecast errors of precipitationare pretty accurate.

Making the Output of Seasonal Climate Models More Palatable to Agriculture: A Copula-Based Postprocessing Method

AbstractSeasonal climate forecasts from raw climate models at coarse grids are often biased and statistically unreliable for credible crop prediction at the farm scale. We develop a copula-based postprocessing (CPP) method to overcome this mismatch problem. The CPP forecasts are ensemble based and are generated from the predictive distribution conditioned on raw climate forecasts. CPP performs univariate postprocessing procedures at each station, lead time, and variable separately and then applies the Schaake shuffle to reorder ensemble sequence for a more realistic spatial, temporal, and cross-variable dependence structure. The use of copulas makes CPP free of strong distributional assumptions and flexible enough to describe complex dependence structures. In a case study, we apply CPP to postprocess rainfall, minimum temperature, maximum temperature, and radiation forecasts at a monthly level from the Australian Community Climate and Earth-System Simulator Seasonal model (ACCESS-S) to three representative stations in Australia. We evaluate forecast skill at lead times of 0–5 months on a cross-validation theme in the context of both univariate and multivariate forecast verification. When compared with forecasts that use climatological values as the predictor, the CPP forecast has positive skills, although the skills diminish with increasing lead times and finally become comparable at long lead times. When compared with the bias-corrected forecasts and the quantile-mapped forecasts, the CPP forecast is the overall best, with the smallest bias and greatest univariate forecast skill. As a result of the skill gain from univariate forecasts and the effect of the Schaake shuffle, CPP leads to the most skillful multivariate forecast as well. Further results investigate whether using ensemble mean or additional predictors can enhance forecast skill for CPP.

Quantifying the Uncertainties in Data-Driven Models for Reservoir Inflow Prediction

Reservoir inflow prediction is subject to high uncertainties in data-driven modelling. In this study, a decomposition scheme is proposed to evaluate the individual and combined contributions of uncertainties from input sets and data-driven models to the total predictive uncertainty. Six variables (i.e., inflow (Q), precipitation (P), relative humidity (H), minimum temperature (Tmin), maximum temperature (Tmax) and precipitation forecast (F)), and three data-driven models (i.e., artificial neural network (ANN), support vector machine (SVM), and adaptive neuro fuzzy inference systems (ANFIS)) are used to produce an ensemble of 10-day inflow forecast for Huanren reservoir in China, and the analysis of variance (ANOVA) method is employed to decompose the uncertainty. The ensemble forecast results show that when the three variables, i.e., Q, P and F, are used only, the predictive accuracy of the data-driven models is very high and the addition of the other three variables, i. e., H, Tmin and Tmax, can slightly improve the predictive accuracy. The decomposition results indicate that the input set is the dominant source of uncertainty, the contribution of the data-driven model is limited and has a strong seasonal variation: larger in winter and summer, smaller in spring and autumn. Most importantly, the interactive contribution of the input set and the data-driven model to the total predictive uncertainty is very high and is more significant than the individual contribution from the model itself, implying that the combined effects of the input set and the data-driven model should be carefully considered in the modelling process.

A Prediction Scheme for Daily Maximum and Minimum Temperature Forecasts Using Recurrent Neural Network and Rough set

A new nonlinear objective prediction scheme has been developed for predicting 24h daily maximum and minimum temperature forecasts at 14 stations in Guangxi, China during Jan, 2015-Jun, 2018 using Recurrent Neural Network (RNN) and based on the daily average, maximum, minimum temperature and precipitation data. Taking the climatology and persistence predictors as primary factors, the conditional attribute reduction method of rough set theory is adopted. By eliminating the unrelated attributes, the predictors direct correlated with the predictand (maximum and minimum temperature) are taken as the RNN model input by means of attribute reduction. This new scheme is validated with 24h short-range forecasts spanning Jan to Jun, 2018. Using identical predictors and sample cases, predictions of the RNN model are compared with the stepwise regression method, and results show that the former is more accurate. The mean absolute errors of RNN at 14 stations in Guangxi are lower than those of the stepwise regression method. The mean forecast accuracy with absolute errors being less than 2°C (1°C) of RNN is higher than that of the stepwise regression method. Moreover, the number of forecast errors larger than 2°C and the system deviation of daily maximum (minimum) temperature prediction are significantly reduced by RNN model, indicating a potentially better operational weather prediction tool.

On the soil moisture memory and influence on coupled seasonal forecasts over Australia

In this study we assess impacts of land-surface initialization on sub-seasonal and seasonal forecast skills from a coupled model named ACCESS-S1 (a seasonal prediction version 1 of the Australian Community Climate and Earth-System Simulator). A series of sensitivity experiments is conducted to explore to what extent the model skill and mean bias can be affected by different land-surface initialisations. By focusing the analysis on the Australian continent, our study tries to address three questions: (1) how strong is soil moisture memory in the model and how realistic is that compared with some observational evidence; (2) how does the soil moisture memory affect surface fluxes; and (3) how these impacts on surface fluxes are translated into the impacts on forecasting rainfall, temperature and atmospheric circulation. Firstly, we run an offline experiment with the ACCESS-S1 land surface model JULES for the period of 1990–2012 using ERA-interim forcing data, with precipitation further adjusted by monthly observations. This produces 23-year soil moisture time series corresponding to “observed” meteorological forcing. Lagged correlations between soil moisture at different levels and at different months are compared with some in-situ observations over Murrumbidgee catchment. We show good agreement between modelled and observed soil moisture coupling between its top-layer and sub-surface root-zone in the southeast part of the continent, and notable soil moisture memory over these regions. We then use the JULES offline soil moisture data to initialize ACCESS-S1 in its 3-month hindcasts with start date of 1st May for the 23-year period. In contrast to the default ACCESS-S1 setup which uses climatological soil moisture in its land-surface initialisation, our results show significant improvements to ACCESS-S1 forecast skill of surface maximum temperature (Tmax) and evapotranspiration by initialising the model with JULES offline data. It also has moderate improvements of surface minimum temperature (Tmin) and precipitation forecasts. The skill gain is particularly evident over the eastern part of the continent where the modelled and observed soil moisture memory is strong. Our study demonstrates that in future forecast system development, we not only need to initialise the model with updated soil moisture anomalous conditions, but also need to make sure these anomalies are combined with correct and consistent soil moisture climatology for both hindcast and real-time forecast.

Application of artificial neural networks to frost detection in central Chile using the next day minimum air temperature forecast

Application of artificial neural networks to frost detection in central Chile using the next day minimum air temperature forecast

VERIFICATION OF MEDIUM RANGE WEATHER FORECAST IN NORTH GUJARAT

The medium range weather forecast issued from NCMRWF, Noida on rainfall, maximum temperature, minimum temperature and wind speed for the last 18 years (1999-2016) has been verified with observed weather parameters recorded at agrometeorological observatory, Sardarkrushinagar to known its accuracy. The results revealed that the usability of rainfall was higher in pre monsoon, post monsoon and winter seasons. However, during monsoon, the accuracy of rainfall forecast was 78 percent with RMSE value of 15.3 that indicated the lower accuracy. The maximum temperature forecast accuracy was very good varied from 76 to 88% in different seasons. Similarly, minimum temperature forecast was excellent in monsoon season (88%), and poor in winter season (57%). The wind speed forecast was excellent in all the seasons.

Short-term forecasting of daily reference evapotranspiration using the Penman-Monteith model and public weather forecasts

Short-term daily reference evapotranspiration (ETo) forecasts are required to facilitate real-time irrigation decision making. We forecasted daily 7-day-ahead ETo using the Penman–Monteith (PM) model and public weather forecasts. Public weather forecast data, including daily maximum and minimum temperatures, weather types and wind scales, for six stations located in a wide range of climate zones of China were collected. Weather types and wind scales were converted to sunshine duration and wind speed to forecast ETo. Meanwhile, daily meteorological data for the same period and locations were collected to calculate ETo, which served as reference standard for evaluating forecasting performance. The results showed that the forecasting performance for the minimum temperature was the best, followed by maximum temperature, sunshine duration and wind speed. Also, it was found that using public weather forecasts and the PM model improved the forecasting performance of daily ETo compared to those obtained when using the HS model with temperature forecasts as the only input data, and this improvement was because the weather type and wind scale forecasts also have positive influence on ETo forecasting. Further, the greatest impact on ETo forecasting error was found to be caused by the errors in sunshine duration and wind speed, followed by maximum and minimum temperature forecasts.

Calibration, Bridging, and Merging to Improve GCM Seasonal Temperature Forecasts in Australia

AbstractThere are a number of challenges that must be overcome if GCM forecasts are to be widely adopted in climate-sensitive industries such as agriculture and water management. GCM outputs are frequently biased relative to observations and their ensembles are unreliable in conveying uncertainty through appropriate spread. The calibration, bridging, and merging (CBaM) method has been shown to be an effective tool for postprocessing GCM rainfall forecasts to improve ensemble forecast attributes. In this study, CBaM is modified and extended to postprocess seasonal minimum and maximum temperature forecasts from the POAMA GCM in Australia. Calibration is postprocessing GCM forecasts using a statistical model. Bridging is producing additional forecasts using statistical models that have other GCM output variables (e.g., SST) as predictors. It is demonstrated that merging calibration and bridging forecasts through CBaM effectively improves the skill of POAMA seasonal minimum and maximum temperature forecasts for Australia. It is demonstrated that CBaM produces bias-corrected forecasts that are reliable in ensemble spread and reduces forecasts to climatology when there is no evidence of forecasting skill. This work will help enable the adoption of GCM forecasts by climate-sensitive industries for quantitative modeling and decision-making.

How accurate are the weather forecasts available to the public in India?

In this study, forecasts from three weather websites (Real Time Weather System, Weather Underground and Accuweather) are evaluated for six Indian regions. Average statistics for all the Indian locations are calculated for the standard weather parameters - maximum and minimum temperature, wind speed, wind direction, pressure and rainfall. The correlation between predictions and synoptic observations is similar for all three forecasters. Bias magnitude is also similar for all three websites. Regional analysis demonstrates that Accuweather predicts maximum temperature best in the Central, Northwest and West regions, and Weather Underground performs well for the East and West regions. Real Time Weather System produces consistently accurate minimum temperature forecasts for all regions.

Error characteristics of temperature forecast in Finland for the period 1979–2011 in relation to various weather patterns

ABSTRACTThe investigation of weather forecast errors has focussed mainly on assessing the physical causes in numerical weather forecasting, such as errors in the initial state and model deficiencies. The framework and context of this paper are different. A 30 year time series (1979–2011) of 1 and 2 day human forecasts of temperature in Finland were used to investigate the statistical and climatological characteristics of maximum and minimum temperature forecast errors. Error dependence on different flow types and weather regimes was also analysed. The error statistics indicate that the forecast root‐mean‐square error has halved, and the forecast hit rate within a 2.5 °C error threshold has increased from 70% to 85–90% during the 30 year period. Large forecast errors of >5 °C are presently very rare, and they are mostly seen as positive biases in extreme cold temperatures and inversions. Otherwise, the forecasts nowadays are mostly unbiased with only small negative biases associated with Foehn events.Forecast error extremes were found during winter, with a secondary peak in spring and under high pressure periods in non‐westerly and especially northeasterly airflows, dry air mass and relatively weak winds. The summertime maximum temperature errors are typically associated with opposite weather situations that occur most typically with cyclonic circulations, moist air masses and strong winds.

Utilization of real-time vineyard observations to produce downscaled temperature forecasts for frost protection operations

Continued drought in California, along with a threatened and endangered salmon population, has required the state of California to control Russian River water usage to protect young, budding winegrape vines during spring frosts. Improved forecasts of the location and duration of frost with increased lead time at individual vineyards could reduce the number of hours of spraying required. In addition, better forecasts could assure adequate supplies of water in off-stream storage systems, reducing demands on tributaries and the threat of fish kills. An automated digital forecast system has been developed and is running routinely to provide vineyard-specific temperature forecasts to growers, commercial frost forecasters, and water managers. The system is based on the use of a high-resolution terrain grid and focuses on real-time hourly observations from 72 vineyards located within the Russian River Basin. The National Weather Service’s (NWS) Graphical Forecast Editor software is configured with a 0.32 km  0.74 km grid spacing, and an objective analysis tool is used to generate a high-resolution observation grid of temperature, dewpoint, and relative humidity—along with computed wet-bulb temperature. The minimum values of each of these parameters also are computed. These observed grids are used, in turn, to bias correct the gridded numerical model and model output statistics guidance grids, as well as the NWS official forecast grids, using a 30-day regression of forecast versus observed values. Verification shows that for the overnight lows (≤2°C), the bias correction improves skill in overnight minimum temperature forecasts by a factor of 3–4 for most forecast guidance used.

English

The determination of variance of surface air temperature is very essential since it has a direct impact on vegetation, environment and human livelihood. Forecast of surface air temperature is difficult because of the complex physical phenomenon and the random-like behavior of atmospheric system which influences the temperature event on the earth surface. In this study, forecast models based on artificial neural network (ANN) and genetic programming (GP) approaches were proposed to predict lead seven days minimum and maximum surface air temperature using the weather parameters observed at the station Chennai, India. The outcome of this study stated that models formulated using ANN approach are more accurate than genetic programming for all seven days with the highest coefficient of determination (R2), least mean absolute error (MAE), root mean square error (RMSE) and mean absolute percentage error (MAPE) on deployment with independent test dataset. ANN models give statistically acceptable mean absolute error of 0.59oC for lead day one in minimum temperature forecast and 0.86oC variance for lead day one in maximum temperature forecast. The study also clarified that the level of accuracy of the proposed prediction models were found to be better for smaller lead days when compared with higher lead days with both approaches.

Mountain hydrometeorology

Mountain hydrometeorology