Application of Kalman Filter Method in the Forecast of Temperature in Nanchang

A temperature forecasting model was created firstly based on the Kalman filter method,and then used to predict the highest and lowest temperature in Nanchang station from October 27 to November 1,2017.Finally,according to the empirical forecasting method,guidance forecasts were established for the northern,central,and southern parts of Nanchang City.After inspection,it was found that the temperature prediction model established based on the Kalman filter method in Nanchang station had good prediction performance,and especially in the 24-hour forecast,it had advantages over the European Center.The accuracy of low temperature forecast was better than that of high temperature forecast.

Analysis and Forecasting Model of Durative Hightemperature in Late Spring and Early Summer in 2009

This paper analyzed characteristics of Pingliang City's continuous hot weather from late spring to early summer in 2009.The result showed that,when the daily maximum temperature in some parts of cities had run up to 32℃ or above,the number of days reached the top in recent 40 years.The average temperature,average maximum temperature,surface maximum temperature and surface average temperature in most parts of the city broke history record.Based on the analysis of characteristics of the 500 hPa circulation,which resulted in durative high temperature weather,3 kinds of the high temperature circulation patterns were summarized.It was the continental warm high pressure that resulted in the durative high temperature weather in June,2009.Meanwhile,by using European numerical forecast product and MOS,the forecasting method of high temperature,from June to August,was set up.The method had been used in June,and its high-temperature forecasting accuracy in 24,48,72,96,120 hours had respectively amounted to 76.6%,69.5%,61.4%,58.1% and 51.9%.

Quantum Atmospheric Biophysics: A Comparison of Four Weather Stations in India on Average Monthly Temperatures Since 1892 and Forecasts to 2150

The identification method revealed asymmetric wavelets of dynamics, as fractal quanta of the behavior of the surface air layer at a height of 2 m, according to the average monthly temperature at four weather stations in India (Srinagar, Jolhpur, New Delhi and Guvahati). For Srinagar station, the maximum for all years is observed in July, for Jolhpur and New Delhi stations it shifts to June, and for Guvahati it shifts to August. With a high correlation coefficient of 0.9659, 0.8640 and 0.8687, a three-factor model of the form was obtained. The altitude, longitude and latitude of the station are given sequentially. The hottest month for Srinagar over a period of 130 years is in July. At the same time, the temperature increased from 23.4 °C to 24.2 °C (by 3.31%). A noticeable decrease in the intensity of heat flows in June occurred at Jolhpur (over 125 years, a decrease from 36.2 °C to 33.3 °C, or by 8.71%) and New Delhi (over 90 years, a decrease from 35.1 °C to 32.4 °C, or by 7.69%). For almost 120 years, Guvahati has experienced complex climate changes: In 1902, the hottest month was July, but in 2021 it has shifted to August. The increase in temperature at various stations is considered. At Srinagar station in 2021, compared to 1892, temperatures increased in June, September and October. Guvahati has a 120-year increase in December, January, March and April. Temperatures have risen in February, March and April at Jolhpur in 125 years, but have risen in February and March at New Delhi Station in 90 years. Despite the presence of tropical evergreen forests, the area around Guvahati Station is expected to experience strong warming.

MULTIMODEL CONSENSUS FORECASTING OF LOW TEMPERATURE AND ICY WEATHER OVER CENTRAL AND SOUTHERN CHINA IN EARLY 2008

Based on the daily mean temperature and 24-h accumulated total precipitation over central and southern China, the features and the possible causes of the extreme weather events with low temperature and icing conditions,which occurred in the southern part of China during early 2008, are investigated in this study. In addition, multimodel consensus forecasting experiments are conducted by using the ensemble forecasts of ECMWF, JMA, NCEP and CMA taken from the TIGGE archives. Results show that more than a third of the stations in the southern part of China were covered by the extremely abundant precipitation with a 50-a return period, and extremely low temperature with a 50-a return period occurred in the Guizhou and western Hunan province as well. For the 24- to 216-h surface temperature forecasts, the bias-removed multimodel ensemble mean with running training period(R-BREM) has the highest forecast skill of all individual models and multimodel consensus techniques. Taking the RMSEs of the ECMWF 96-h forecasts as the criterion, the forecast time of the surface temperature may be prolonged to 192 h over the southeastern coast of China by using the R-BREM technique. For the sprinkle forecasts over central and southern China, the R-BREM technique has the best performance in terms of threat scores(TS) for the 24- to 192-h forecasts except for the 72-h forecasts among all individual models and multimodel consensus techniques. For the moderate rain, the forecast skill of the R-BREM technique is superior to those of individual models and multimodel ensemble mean.

STUDY ON THE METHOD FOR FORECASTING DANGEROUSNESS OF COAL FACE AND HEADING FACE OUTBURST BY TWO TEMPERATURE INDEXES

In line with the sensitivity of coal drillings temperature and coalbed temperature to the dangerous zone of coal and gas outburst, two temperature sensitive indexes (△Tm, △tm) for forecasting dangerousness of coal face and heading face outburst are defined, and deal with the foundation on drillings and coalbed temperatures used as sensitive indexes and the principle and method of determining drillings and coalbed temperatures. On the basis of this, we put forward the method for forecasting dangerousness of coal face and heading face outburst by two temperature sensitive indexes and determine the critical values of two temperature sensitive indexes (△Tm= 5.5℃, △tm = 4.5℃) by in-situ observation and requirement for determining sensitive index.

Big Data Analytics Using Swarm-Based Long Short-Term Memory for Temperature Forecasting

In the past few decades,climatic changes led by environmental pollution,the emittance of greenhouse gases,and the emergence of brown energy utilization have led to global warming.Global warming increases the Earth’s temperature,thereby causing severe effects on human and environmental conditions and threatening the livelihoods of millions of people.Global warming issues are the increase in global temperatures that lead to heat strokes and high-temperature-related diseases during the summer,causing the untimely death of thousands of people.To forecast weather conditions,researchers have utilized machine learning algorithms,such as autoregressive integrated moving average,ensemble learning,and long short-term memory network.These techniques have been widely used for the prediction of temperature.In this paper,we present a swarm-based approach called Cauchy particle swarm optimization(CPSO)to find the hyperparameters of the long shortterm memory(LSTM)network.The hyperparameters were determined by minimizing the LSTM validationmean square error rate.The optimized hyperparameters of the LSTM were used to forecast the temperature of Chennai City.The proposed CPSO-LSTM model was tested on the openly available 25-year Chennai temperature dataset.The experimental evaluation on MATLABR2020a analyzed the root mean square error rate and mean absolute error to evaluate the forecasted output.The proposed CPSO-LSTM outperforms the traditional LSTM algorithm by reducing its computational time to 25 min under 200 epochs and 150 hidden neurons during training.The proposed hyperparameter-based LSTM can predict the temperature accurately by having a root mean square error(RMSE)value of 0.250 compared with the traditional LSTM of 0.35 RMSE.

Application Research of Temperature Forecasts on Elman Neural Network

[Objective] The aim was to establish Elman neural network model to predict the dynamic changes of temperature. [Method] Considering the inherent nature of temperature, and dy dint of the temperature in Chongqing during 1951-2010, the Elman artificial neural network model was applied to predict the temperature. [Result] This simulation result suggested that the relative error was small and can have a good simulation to the future temperature changes. [Conclusion] The prediction result can guide agricultural production and further apply to the field of pricing the weather derivative products.

Characters of Temperature Variation and Minimal Temperature Forecast inside of Solar Greenhouse in Winter in Shouguang City of Shandong Province

[Objective] The aim was to improve meteorological service of protected agriculture and to reduce effects of meteorological disasters. [Method] Characters of temperature variation in solar greenhouse and minimal temperature forecast models in winter were analyzed based on meteorological data inside and outside of solar greenhouse in winter during 2008-2011, as per correlation and stepwise regression method. [Result] Temperature was of significant changes in solar greenhouse in sunny and cloudy days and the change was higher in sunny days. In overcast days, temperature in solar greenhouse was lower and plants were affected seriously. In addition, the minimal temperature was of good correlation with outside temperature and humidity, temperature and soil temperature in greenhouse. [Conclusion] The minimal temperature forecast model of solar greenhouse is established and the average absolute error of the forecasted minimums in different types of weather was less than 1 ℃ and the average relative error was lower than 10%.

A Model Output Machine Learning Method for Grid Temperature Forecasts in the Beijing Area

In this paper, the model output machine learning (MOML) method is proposed for simulating weather consultation, which can improve the forecast results of numerical weather prediction (NWP). During weather consultation, the forecasters obtain the final results by combining the observations with the NWP results and giving opinions based on their experience. It is obvious that using a suitable post-processing algorithm for simulating weather consultation is an interesting and important topic. MOML is a post-processing method based on machine learning, which matches NWP forecasts against observations through a regression function. By adopting different feature engineering of datasets and training periods, the observational and model data can be processed into the corresponding training set and test set. The MOML regression function uses an existing machine learning algorithm with the processed dataset to revise the output of NWP models combined with the observations, so as to improve the results of weather forecasts. To test the new approach for grid temperature forecasts, the 2-m surface air temperature in the Beijing area from the ECMWF model is used. MOML with different feature engineering is compared against the ECMWF model and modified model output statistics (MOS) method. MOML shows a better numerical performance than the ECMWF model and MOS, especially for winter. The results of MOML with a linear algorithm, running training period, and dataset using spatial interpolation ideas, are better than others when the forecast time is within a few days. The results of MOML with the Random Forest algorithm, year-round training period, and dataset containing surrounding gridpoint information, are better when the forecast time is longer.

The Impact of AIRS Atmospheric Temperature and Moisture Profiles on Hurricane Forecasts: Ike(2008) and Irene(2011)

Atmospheric InfraRed Sounder （AIRS） measurements are a valuable supplement to current observational data,especially over the oceans where conventional data are sparse.In this study,two types of AIRS-retrieved temperature and moisture profiles,the AIRS Science Team product （SciSup） and the single field-of-view （SFOV） research product,were evaluated with European Centre for Medium-Range Weather Forecasts （ECMWF） analysis data over the Atlantic Ocean during Hurricane Ike （2008） and Hurricane Irene （2011）.The evaluation results showed that both types of AIRS profiles agreed well with the ECMWF analysis,especially between 200 hPa and 700 hPa.The average standard deviation of both temperature profiles was approximately 1 K under 200 hPa,where the mean AIRS temperature profile from the AIRS SciSup retrievals was slightly colder than that from the AIRS SFOV retrievals.The mean SciSup moisture profile was slightly drier than that from the SFOV in the mid troposphere.A series of data assimilation and forecast experiments was then conducted with the Advanced Research version of the Weather Research and Forecasting （WRF） model and its three-dimensional variational （3DVAR） data assimilation system for hurricanes Ike and Irene.The results showed an improvement in the hurricane track due to the assimilation of AIRS clear-sky temperature profiles in the hurricane environment.In terms of total precipitable water and rainfall forecasts,the hurricane moisture environment was found to be affected by the AIRS sounding assimilation.Meanwhile,improving hurricane intensity forecasts through assimilating AIRS profiles remains a challenge for further study.

Skillful Seasonal Forecasts of Summer Surface Air Temperature in Western China by Global Seasonal Forecast System Version 5

Variations of surface air temperature （SAT） are key in affecting the hydrological cycle, ecosystems and agriculture in western China in summer. This study assesses the seasonal forecast skill and reliability of SAT in western China, using the GloSea5 operational forecast system from the UK Met Office. Useful predictions are demonstrated, with considerable skill over most regions of western China. The temporal correlation coefficients of SAT between model predictions and observations axe larger than 0.6, in both northwestern China and the Tibetan Plateau. There are two important sources of skill for these predictions in western China： interannual variation of SST in the western Pacific and the SST trend in the tropical Pacific. The tropical SST change in the recent two decades, with a warming in the western Pacific and cooling in the eastern Pacific, which is reproduced well by the forecast system, provides a large contribution to the skill of SAT in northwestern China. Additionally, the interannual variation of SST in the western Pacific gives rise to the reliable prediction of SAT around the Tibetan Plateau. It modulates convection around the Maritime Continent and further modulates the variation of SAT on the Tibetan Plateau via the surrounding circulation. This process is evident irrespective of detrending both in observations and the model predictions, and acts as a source of skill in predictions for the Tibetan Plateau. The predictability and reliability demonstrated in this study is potentially useful for climate services providing early warning of extreme climate events and could imply useful economic benefits.

Change Characteristics and Forecast Research of Road Surface Temperature on Huyu Expressway (Hubei Section)

In this paper,the monitoring data of road surface temperature,air temperature,wind speed,wind direction,relative humidity and precipitation from the automatic weather stations of Hurongxi,Hanyi,Wuhuang and Huanghuang on Huyu expressway from June 2013 to August 2014 were used to investigate the change characteristics of different sections' road surface temperatures in different seasons and sky conditions. The forecast models of the maximum and minimum road surface temperatures were established on different sections by statistical analysis methods,and the forecast results were verified. The results showed that the road surface temperature and air temperature of Hurongxi,Hanyi,Wuhuang and Huanghuang displayed obvious diurnal variation,but the difference between the road surface temperature and air temperature was larger. Compared with the other three sections,the maximum difference between the road surface temperature and air temperature on Hurongxi increased in winter and decreased in summer overall. The road surface temperature was close to air temperature on Hurongxi after sunset on sunny to cloudy and overcast in winter,while less than air temperature on Hanyi,Wuhuang and Huanghuang. The air temperature was less than road surface temperature on the four sections on rainy day and significant on Hurongxi. In summer,the air temperature was less than road surface temperature on the four sections under three sky conditions and the difference between them in afternoon was the biggest on sunny to cloudy and overcast. The road surface temperature was very close to each other among the four sections in January,while which was rising with the decrease of altitude in April,July and October. The forecast result of the road surface temperature was close to actual result on Hurongxi and Huanghuang,so which can be for reference. But there were some big errors between the forecast result and actual result in several timings on Hanyi and Wuhuang,so the forecast result should be corrected for actual business work.

Study on Refined Forecast Method of Daily Maximum Temperature in Wugang City from July to September

[Objective] The aim was to study the refined forecast method of daily highest temperature in Wugang City from July to September. IM[ethod] By dint of ECMWF mode product and T231 in 2009 and 2010 and daily maximum temperature in the station in corresponding period, multi-factors similar forecast method to select forecast sample, multivariate regression multi-mode integration MOS method, after dynamic corrected mode error and regression error, dynamic forecast equation was concluded to formulate the daily maximum temperature forecast in 24 -120 h in Wugang City from July to September. [ Result] Through selection, error correction, the daily maximum temperature equation in Wugang City from July to September was concluded. Through multiple random sampling, F test was made to pass test with significant test of 0.1. [ Conclusionl The method integrated domestic and foreign forecast mode, made full use of useful information of many modes, absorbed each others advantages, con- sidered local regional environment, lessen mode and regression error, and improved forecast accuracy.

Bias correction of sea surface temperature retrospective forecasts in the South China Sea

Offline bias correction of numerical marine forecast products is an effective post-processing means to improve forecast accuracy. Two offline bias correction methods for sea surface temperature(SST) forecasts have been developed in this study: a backpropagation neural network(BPNN) algorithm, and a hybrid algorithm of empirical orthogonal function(EOF) analysis and BPNN(named EOF-BPNN). The performances of these two methods are validated using bias correction experiments implemented in the South China Sea(SCS), in which the target dataset is a six-year(2003–2008) daily mean time series of SST retrospective forecasts for one-day in advance, obtained from a regional ocean forecast and analysis system called the China Ocean Reanalysis(CORA),and the reference time series is the gridded satellite-based SST. The bias-correction results show that the two methods have similar good skills;however, the EOF-BPNN method is more than five times faster than the BPNN method. Before applying the bias correction, the basin-wide climatological error of the daily mean CORA SST retrospective forecasts in the SCS is up to-3°C;now, it is minimized substantially, falling within the error range(±0.5°C) of the satellite SST data.

A Preliminary Study on the Use of NCEP Temperature Images and Additive Tectonic Stress from Astro-Tidal-Triggering to Forecast Short-Impending Earthquakes

Taking the three earthquakes which occurred in Tibet, China during the period of July 12 to August 25, 2004 as an example,the paper analyses the M_S≥6.0 earthquakes that occurred in China and M_S≥7.0 earthquakes that occurred overseas since May of 2003 by combining the image data from the National Center for Environmental Prediction of America(NCEP)with the additive tectonic stress from astro-tidal-triggering (ATSA) and makes the following conclusions: The abnormal temperature image data of NCEP can better reflect the spatial-temporal evolution process of tectonic earthquake activity; The ATSA has an evident triggering effect on the activity of a fault when the terra stress is in critical status; using the NCEP images and the ATSA to forecast short-impending earthquake is a new concept; The three earthquakes occurred during the same phase of the respective ATSA cycle, i.e. that occurred at the time when the ATSA reached the relatively steady end of a peak, rather than at the time when the variation rate was maximal. In addition, the author discovered that the occurrence time of other earthquake cases during 2003～2004 in Tibet was also in the same phase of the above-mentioned cycles, and therefore, further study of this feature is needed with more earthquake cases in other areas over longer periods of time.

ConvLSTM Based Temperature Forecast Modification Model for North China

The correction of model forecast is an important step in evaluating weather forecast results.In recent years,post-processing models based on deep learning have become prominent.In this paper,a deep learning model named EDConvLSTM based on encoder-decoder structure and ConvLSTM is developed,which appears to be able to effectively correct numerical weather forecasts.Compared with traditional post-processing methods and convolutional neural networks,ED-ConvLSTM has strong collaborative extraction ability to effectively extract the temporal and spatial features of numerical weather forecasts and fit the complex nonlinear relationship between forecast field and observation field.In this paper,the post-processing method of ED-ConvLSTM for 2 m temperature prediction is tested using The International Grand Global Ensemble dataset and ERA5-Land data from the European Centre for Medium-Range Weather Forecasts(ECMWF).Root mean square error and temperature prediction accuracy are used as evaluation indexes to compare ED-ConvLSTM with the method of model output statistics,convolutional neural network postprocessing methods,and the original prediction by the ECMWF.The results show that the correction effect of EDConvLSTM is better than that of the other two postprocessing methods in terms of the two indexes,especially in the long forecast time.

Application of Machine-Learning-Based Objective Correction Method in the Intelligent Grid Maximum and Minimum Temperature Predictions

Post-processing correction is an effective way to improve the model forecasting result. Especially, the machine learning methods have played increasingly important roles in recent years. Taking the meteorological observational data in a period of two years as the reference, the maximum and minimum temperature predictions of Shenyang station from the European Center for Medium-Range Weather Forecasts (ECMWF) and national intelligent grid forecasts are objectively corrected by using wavelet analysis, sliding training and other technologies. The evaluation results show that the sliding training time window of the maximum temperature is smaller than that of the minimum temperature, and their difference is the largest in August, with a difference of 2.6 days. The objective correction product of maximum temperature shows a good performance in spring, while that of minimum temperature performs well throughout the whole year, with an accuracy improvement of 97% to 186%. The correction effect in the central plains is better than in the regions with complex terrain. As for the national intelligent grid forecasts, the objective correction products have shown positive skills in predicting the maximum temperatures in spring (the skill-score reaches 0.59) and in predicting the minimum temperature at most times of the year (the skill-score reaches 0.68).

Interpretation of 850 hPa of Temperature in Temperature Forecast

Special geographical location,topography and landform in Xinzhou region decide different climate characteristics in the area under its jurisdiction.Different change characteristics of temperature increase its forecast difficulty.To understand occurrence time of daily highest and lowest temperature,Xinfu District was taken as research area,and actual lowest and highest temperature at automatic station of Xinfu District in 2014 was used.Analytic results showed that occurrence time of daily highest and lowest temperature was different in different seasons,and distribution characteristics of daily highest and lowest temperature were also different in each region.Moreover,forecast method and idea were proposed.

Short-time Forecast Method of Winter Minimum Temperature in the Northern Area of Fujian

[Objective] The research aimed to study the short-time forecast method of winterminimum temperature in the northern area of Fujian.[Method] By analyzing the variation trends and distribution characteristics of extremely and averageminimum temperatures in northern Fujian in winter during 1969-2008,the relative meteorological factors which affected the low temperature weather in winter were found.The influences of relative meteorological factors on winterminimum temperature and the forecast method were summarized by combining with the climate characteristics in northern Fujian.[Result] Winterminimum temperature in Guangze and Pucheng in the north of northern Fujian was the lowest.The second one was in Shaowu,Wuyishan,Jianyang,Songxi and Zhenghe.Theminimum temperature in Jian’ou and Shunchang was higher and was the highest in Yanping.Theminimum temperature mainly depended on the temperature reduction degree from the afternoon to the night.The temperature reduction degree varied with the sky condition and cold air intensity.The temperature reduction included the advection,radiation,advection-radiation and non-advection-radiation types.The temperature had the different reduction characteristics under the different sky conditions.The forecast ofminimum temperature should be carried out based on the weather typing.Meanwhile,the successful forecast key ofminimum temperature was grasping the shift pathway and speed of cold air.[Conclusion] The research provided the theory basis for improving the forecast accuracy of winterminimum temperature.

Forecasts of South China Sea surface temperature anomalies using the Nio indices and dipole mode index as predictors

Based on an empirical orthogonal function （EOF） analysis of the monthly NCEP Optimum Interpolation Sea Surface Temperature （OISST） data in the South China Sea （SCS） after removing the climatological mean and trends of SST, over the period of January 1982 to October 2003, the corresponding TCF correlates best with the Dipole Mode Index （DMI）, Nino1＋2, Nino3.4, Nino3, and Niflo4 indices with time lags of 10, 3, 6, 5, and 6 months, respectively. Thus, a statistical hindcasts in the prediction model are based on a canonical correlation analysis （CCA） model using the above indices as predictors spanning from 1993/1994 to 2003/2004 with a 1-12 month lead time after the canonical variants are calculated, using data from the training periods from January 1982 to December1992. The forecast model is successful and steady when the lead times are 1-12 months. The SCS warm event in 1998 was successfully predicted with lead times from 1-12 months irrespective of the strength or time extent. The prediction ability for SSTA is lower during weak ENSO years, in which other local factors should be also considered as local effects play a relatively important role in these years. We designed the two forecast models： one using both DMI and Nino indices and the other using only Nino indices without DMI, and compared the forecast accuracies of the two cases. The spatial distributions of forecast accuracies show different confidence areas. By turning off the DMI, the forecast accuracy is lower in the coastal areas off the Philippines in the SCS, suggesting some teleconnection may occur with the Indian Ocean in this area. The highest forecast accuracies occur when the forecast interval is five months long without using the DMI, while using both of Nino indices and DMI, the highest accuracies occur when the forecast interval time is eight months, suggesting that the Nino indices dominate the interannual variability of SST anomalies in the SCS. Meanwhile the forecast accuracy is evaluated over an independent test period of more than 11 years （1993/94 to October 2004） by comparing the model performance with a simple prediction strategy involving the persistence of sea surface temperature anomalies over a 1-12 month lead time （the persisted prediction）. Predictions based on the CCA model show a significant improvement over the persisted prediction, especially with an increased lead time （longer than 3 months）. The forecast model performs steadily and the forecast accuracy, i.e., the correlation coefficients between the observed and predicted SSTA in the SCS are about 0.5 in most middle and southern SCS areas, when the thresholds are greater than the 95% confidence level. For all 1 to 12 month lead time forecasts, the root mean square errors have a standard deviation of about 0.2. The seasonal differences in the prediction performance for the 1-12 month lead time are also examined.