Air Temperature Forecasts Research Articles

Evaluation of temperature-based machine learning and empirical models for predicting daily global solar radiation

Accurate global solar radiation data are fundamental information for the allocation and design of solar energy systems. The current study compared different machine learning and empirical models for global solar radiation prediction only using air temperature as inputs. Four machine learning models, e.g., hybrid mind evolutionary algorithm and artificial neural network model, original artificial neural network, random forests and wavelet neural network, as well as four empirical temperature-based models (Hargreaves-Samani model, Bristow-Campbell model, Jahani model, and Fan model) were applied for prediction of daily global solar radiation in temperate continental regions of China. The results indicated the hybrid mind evolutionary algorithm and artificial neural network model provided better estimations, compared with the existing machine learning and empirical models. Thus, the temperature-based hybrid model is highly recommended to predict global solar radiation in temperate continental regions of China when only air temperature data are available. Combining the hybrid model with future air temperature forecasts, we can get the accurate information of future solar radiation, which is of great importance to management and operation of solar energy systems.

Performance of Post-Processed Methods in Hydrological Predictions Evaluated by Deterministic and Probabilistic Criteria

Meteorological Ensemble Streamflow Prediction (ESP), which uses Ensemble Weather forecasts (EWFs) to drive hydrological models, is a useful methodology for extending forecast periods and to provide valuable uncertainty information to improve the operation of future water resources. However, raw EWFs are usually biased and under-dispersive and so cannot be directly used in ESP, leading to the development of several post-processing methods. The performance of these methods needs to be evaluated/compared in building ESP based on deterministic and probabilistic criteria. In addition, likely influencing factors also need to be identified. This study evaluated the performance of four state-of-the-art methods: the Generator-based Post-Processing (GPP) method, Extended Logistic Regression (ExLR), Bayesian Model Averaging (BMA) and Affine Kernel Dressing (AKD), using a simple bias correction (BC) method as a benchmark. The evaluation was carried out over four watersheds with different basin areas in the humid region of central-south China based on the weather reforecasts from the Global Ensemble Forecasting System (GEFS). The results show that the performance of the post-processing methods varies with the forecast variable (precipitation, or air temperature or streamflow), but all of them outperform the BC and GEFS. For the four post-processing methods, the advantage of the generator-based methods (GPP and ExLR) lies in their probabilistic performance, which outperforms the distribution-based methods (BMA and AKD) by about 10% in precipitation forecasts and about 20% in streamflow forecasts, while the distribution-based methods (BMA and AKD) are better at their deterministic performance for precipitation forecasts, with a benefit of about 15%. Meanwhile, the post-processing methods generally perform better for precipitation and streamflow forecasts, but worse for air temperature forecasts for a bigger basin compared to the distribution-based methods. The results of this study emphasize the importance of considering the uncertainty of post-processing methods in ESP.

Development and evaluation of a real-time forecasting framework for daily water quality forecasts for Lake Chaohu to Lead time of six days

The socioeconomic benefits associated with informative water quality forecasts for large lakes are becoming increasingly evident. However, it remains an enormous challenge to produce forecasts of water quality variables that are accurate enough to meet public demand. In this study, we developed and evaluated a new forecast framework for real-time forecasting of daily dissolved oxygen (DO), ammonium nitrogen (NH), total phosphorus (TP) and total nitrogen (TN) concentrations at lead times from one to six days for Lake Chaohu, the fifth largest freshwater lake in China. The forecast framework is based on a 3-D hydrodynamic ecological model referred to as EcoLake. We used hydrological, meteorological and water quality data from multiple sources to generate initial conditions and forcing functions. Solar radiation and inflows from tributaries which are not readily available were calculated using forecasted cloud cover and rainfall. Forecast skill was evaluated based on 122 forecasts produced on different days in 2017 and for each of the 12 sampling sites. Results indicate that the skill of the forecast framework varies considerably across water quality variables, sampling sites, and lead times. Generally, the forecast framework is more skillful than the persistence forecasts, which use the most recent observations as forecasts. The TN forecasts tend to be the most skillful with a mean RMSE skill score of 28.5% averaged across the six lead times. The DO forecasts tend to have the lowest skill with an average value of 10.9%. Model sensitivity experiments further revealed that errors in the raw air temperature and wind speed forecasts have a noticeable impact on the overall skill of DO and NH forecasts. The forecast framework proposed here could be a useful operational forecasting tool to enhance the effectiveness of the drinking water supply and public health protection based on the water quality management of Lake Chaohu.

Assessment of the possibilities of the method of analogues for the current forecast of air temperature

Assessment of the possibilities of the method of analogues for the current forecast of air temperature

Monthly and Seasonal Prediction of Precipitation and Air Temperature in the Amur River Basin

The ability to forecast air temperature and precipitation at monthly-seasonal time scale in the Amur River basin is assessed. The climatic characteristics of actual monthly values of air temperature and total precipitation are compared with those based on the semi-Lagrangian global finite-difference model (SL-AV) of general atmospheric circulation for the Amur basin. It is shown that there is high correlation between total precipitation for 1–2 months and water discharge in the Amur River in next months during the warm half-year. The SL-AV model forecasts of air temperature and precipitation in the Amur basin are verified. It is demonstrated that the accuracy of prediction of total precipitation averaged over the Amur basin is higher as compared with that for initial gridded forecasts. The accuracy of such forecasts is rather high for July and August being critical months for the flood formation. The procedure for the statistical correction of monthly and seasonal forecasts of air temperature and precipitation based on the SL-AV model and the regression scheme for the Amur runoff prediction are proposed.

Streamflow forecast sensitivity to air temperature forecast calibration for 139 Norwegian catchments

Abstract. In this study, we used meteorological ensemble forecasts as input to hydrological models to quantify the uncertainty in forecasted streamflow, with a particular focus on the effect of temperature forecast calibration on the streamflow ensemble forecast skill. In catchments with seasonal snow cover, snowmelt is an important flood-generating process. Hence, high-quality air temperature data are important to accurately forecast streamflows. The sensitivity of streamflow ensemble forecasts to the calibration of temperature ensemble forecasts was investigated using ensemble forecasts of temperature from the European Centre for Medium-Range Weather Forecasts (ECMWF) covering a period of nearly 3 years, from 1 March 2013 to 31 December 2015. To improve the skill and reduce biases of the temperature ensembles, the Norwegian Meteorological Institute (MET Norway) provided parameters for ensemble calibration, derived using a standard quantile mapping method where HIRLAM, a high-resolution regional weather prediction model, was used as reference. A lumped HBV (Hydrologiska Byråns Vattenbalansavdelning) model, distributed on 10 elevation zones, was used to estimate the streamflow. The results show that temperature ensemble calibration affected both temperature and streamflow forecast skill, but differently depending on season and region. We found a close to 1:1 relationship between temperature and streamflow skill change for the spring season, whereas for autumn and winter large temperature skill improvements were not reflected in the streamflow forecasts to the same degree. This can be explained by streamflow being less affected by subzero temperature improvements, which accounted for the biggest temperature biases and corrections during autumn and winter. The skill differs between regions. In particular, there is a cold bias in the forecasted temperature during autumn and winter along the coast, enabling a large improvement by calibration. The forecast skill was partly related to elevation differences and catchment area. Overall, it is evident that temperature forecasts are important for streamflow forecasts in climates with seasonal snow cover.

Forecasting wildlife die‐offs from extreme heat events

AbstractExtreme heat events pose increasing challenges to biodiversity conservation worldwide, yet our ability to predict the time, place and magnitude of their impacts on wildlife is limited. Extreme heat events in Australia are known to kill thousands of flying‐foxes (Pteropus spp.), and such die‐offs are expected to become more frequent and widespread in the future under anthropogenic climate change. There is a growing need for predicting when and where such heat‐related die‐offs would occur, to facilitate short‐term wildlife management and conservation actions. In this study, we used gridded hourly air temperature forecasts [Australian Community Climate and Earth‐System Simulator (ACCESS‐R) Numerical Weather Prediction (NWP) model] from the Australian Bureau of Meteorology to predict flying‐fox heat‐related mortality based on an empirically determined threshold of 42.0°C. We tested the accuracy and precision of this model using a twofold evaluation of the ACCESS‐R NWP forecast air temperature during a recorded extreme heat event with in situ air temperature measurements and interpolated weather station data. While our results showed a slight discrepancy between the modelled and measured air temperatures, there was no significant difference in the forecast's accuracy to predict die‐offs during an extreme heat event and the overall summer period. We evaluated the accuracy of mortality predictions based on different air temperature thresholds (38.0, 40.0, 42.0 and 44.0°C). Our results revealed a significant probability of flying‐fox mortality occurrence when forecast air temperature was ≥42.0°C, while the 24‐ and 48‐h forecasts accurately predicted 77 and 73% of the die‐offs, respectively. Thus, the use of 42.0°C forecast air temperature from the ACCESS‐R NWP model can predict flying‐fox mortality reliably at the landscape scale. In principle, the forecaster can be used for any species with known thermal tolerance data and is therefore a promising new tool for prioritizing adaptation actions that aim to conserve biodiversity in the face of climate change.

Distribution and forecast of air temperature in determining of heat output of the district heating substation with heat storage

The aim of this article was to determine the energetic effect of adapting the control method of district heating substation to a solution using dispersed heat storage. The current control method of district heating substation uses the equations for regulating the temperature distribution of the installation water based on the value of the external temperature. This control method causes a significant amount of heat not consumed by end users. To adapt the control method to a solution using dispersed heat storage the following values were analyzed: the forecast of external temperature and duration of episodes with the lowest external temperatures on the heat output of the district heating substation based on 63 heating seasons. Obtained results were presented for a district heating substation in Poland. The analysis shows that a temperature of -15°C can be adopted as the reference to determine the expected heat output of the district heating substation. On this basis the demand for heat output for central heating purposes was 406 kW instead of 430 kW. The energetic effect of use dispersed heat storage to compensate the reduced heat output of the district heating substation was 24 kW with a compensation time of 24.2 h.

基于BMA方法的地面气温概率预报研究

基于BMA方法的地面气温概率预报研究

МЕТОДИКА ПРОСТОВОГО ПРОГНОЗУВАННЯ СТРОКІВ ПОЧАТКУ ТА ПРОХОДЖЕННЯ МАКСИМАЛЬНИХ ВИТРАТ ВОДИ ВЕСНЯНИХ ВОДОПІЛЬ

Introduction. The problem of long-term and prognostic assessment of the terms of spring floods on rivers is relevant in hydrological investigations. Due to climate warming, the flood tides shifted to early dates or the beginning of the calendar year. The purpose is to analyze the long-term passage of spring floods and to carry out their spatial mapping forecasting on the Dnieper (within Ukraine). Methods. Investigation of the dynamics of long-term changes in the timing of spring floods and the use of the forecast method based on the meteorological forecast of ten-day air temperature in the winter-spring season in the Dnieper basin. Results. The method of forecasting the dates of starting and passing the maximum water discharge (levels) is based on the regional equations of the forecast scheme and allows to make a preliminary estimation of flood terms each year, regardless of the availability of meteorological observations. Assessment of the forecasting methodology allows to recommend it as a consultation, and the earliness of forecast varies depending on the geographical location of the catchments and the current weather conditions of a particular spring. Conclusion. The methodology was implemented on the example of spring floods 2017-2018. The overall forecasts were satisfactory, but the quality of the forecasts was affected by the non-simultaneous terms of the spring flood formation in the Dnieper basin. The prognostic probabilities of the dates of spring flood allow characterizing the frequency of their occurrence over many years. On the example of the 2017-2018 spring flood, the rivers of the Dnieper basin found that, in a changing climate, floods can be shifted to earlier and later ones.

수치모델의 계통 오차 분석에서 자기조직화지도 기법의 활용 : 기상청 전지구예보시스템의 여름철 지상기온 예측

본 연구는 자기조직화지도(Self―Organizing Maps, SOM)를 활용하여 수치모델의 계통 오차를 분석하는 방안을 제시하고자, 기상청의 전지구예보시스템에서 북반구의 여름철 지상기온 5일 예측 오차를 분류하고 관련 기상변수를 분석하였다. SOM의 4개 군집으로 분류하면, 극지역에 위치한 제 1 군집의 한랭 오차는 총 운량과 토양 수분의 양의 오차와 함께 나타났다. 제 2군집에서 해양의 지상기온 한랭 오차는 총 운량의 양의 오차와 특히, 해수면 온도의 음의 오차와 상관성이 높았다. 제 3군집은 유의미한 결과를 보이지 않는 지역이다. 마지막으로 유라시아와 북아메리카에서 온난오차를 보였던 제 4 군집은 토양수분 과소모의에 의한 대기―지면과정과 관련되어 있을 것이다. SOM의 군집 분류는 지역별 계통 오차 특징을 공간 평균장대비 상세하게 객관적으로 분류할 수 있다. 또한, 산출된 군집별로 다른 기상변수와의 관계를 시공간적으로 분석함으로써 오차의 원인을 효과적으로 추정할 수 있었다. 따라서 SOM은 수치모델의 계통 오차를 객관적으로 진단하는데 효과적인 방법으로 활용될 수 있을 것이다.

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

Operative Scheme for the Short-range Complex Forecasting of Surface Air Temperature and Humidity

The statistical scheme is proposed for the forecast of surface air temperature and humidity using operative weather forecasts with 3–5-day lead time from the best forecasting hydrodynamic models as well as the archives of forecasts of these models and observational data from 2800 weather stations of Russia, Eastern Europe, and Central Asia. The output of the scheme includes the forecasts of air temperature for the standard observation moments with the period of 6 hours and extreme temperatures with the lead times of 12–120 hours. The accuracy of temperature and humidity forecasts for the period from July 2014 till June 2017 is much higher than that for the forecasts of original hydrodynamic models. The skill scores for extreme temperature forecasts based on the proposed method are compared with the similar results of the Weather Element Computation (WEC) forecasting scheme and forecasts by weathermen.

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 are 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.

Sensitivity of Soil Moisture Analyses to Contrasting Background and Observation Error Scenarios

Soil moisture is a crucial variable for numerical weather prediction. Accurate, global initialization of soil moisture is obtained through data assimilation systems. However, analyses depend largely on the way observation and background errors are defined. In this study, a wide range of short experiments with contrasted specifications of the observation error and soil moisture background were conducted. As observations, screen-level variables and brightness temperatures from the Soil Moisture and Ocean Salinity (SMOS) mission were used. The region of interest is North America, given the good availability of in situ observations and mixture of different climates, making it a good test for global applications. The impact of these experiments on soil moisture and the atmospheric layer near the surface were evaluated. The results highlighted the importance of assimilating observations sensitive to soil moisture for air temperature and humidity forecasts. The benefits on predicting the soil water content were more noticeable with increasing the SMOS observation error, and with the introduction of soil texture dependency in the soil moisture background error.

A Preliminary Assessment of the Impact of Assimilating Satellite Soil Moisture Data Products on NCEP Global Forecast System

It is well documented that soil moisture has a strong impact on precipitation forecasts of numerical weather prediction models. Several microwave satellite soil moisture retrieval data products have also been available for applications. However, these observational data products have not been employed in any operational numerical weather or climate prediction models. In this study, a preliminary test of assimilating satellite soil moisture data products from the NOAA-NESDIS Soil Moisture Operational Product System (SMOPS) into the NOAA-NCEP Global Forecast System (GFS) is conducted. Using the ensemble Kalman filter (EnKF) introduced in recent year publications and implemented in the GFS, the multiple satellite blended daily global soil moisture data from SMOPS for the month of April 2012 are assimilated into the GFS. The forecasts of surface variables, anomaly correlations of isobar heights, and precipitation forecast skills of the GFS with and without the soil moisture data assimilation are assessed. The surface and deep layer soil moisture estimates of the GFS after the satellite soil moisture assimilation are found to have slightly better agreement with the ground soil moisture measurements at dozens of sites across the continental United States (CONUS). Forecasts of surface humidity and air temperature, 500 hPa height anomaly correlations, and the precipitation forecast skill demonstrated certain level of improvements after the soil moisture assimilation against those without the soil moisture assimilation. However, the methodology for the soil moisture data assimilation into operational GFS runs still requires further development efforts and tests.

Impact of Using Near Real-Time Green Vegetation Fraction in Noah Land Surface Model of NOAA NCEP on Numerical Weather Predictions

Green vegetation fraction (GVF) is one of the input parameters of the Noah land surface model (LSM) that is the land component of a number of operational numerical weather prediction (NWP) models at the National Centers for Environmental Prediction (NCEP) of NOAA. The Noah LSM in current NCEP operational NWP models has been using static multiyear averages of monthly GVF derived from satellite observations of NOAA Advanced Very High Resolution Radiometer (AVHRR) normalized difference vegetation index. The multiyear averages of GVF are evidently not the representative of actual conditions of the land surface vegetation cover. This study used a near-real-time (NRT) GVF data set generated from the 8-day composite of the leaf area index product from the Moderate Resolution Imaging Spectroradiometer (MODIS) to assess the impact of NRT GVF on off-line Noah LSM simulations and NWP forecast model. Simulations of the off-line Noah LSM in the Land Information System (LIS) and weather forecasts of the NASA-Unified Weather and Research Forecasting (NUWRF) were obtained using either the static multiyear average AVHRR GVF data set or the NRT MODIS GVF while meteorological forcing data and other settings were kept the same. The off-line simulations and WRF forecasts were then compared against in situ measurements or reanalysis products to assess the impact of using NRT GVF. Improvements of both soil moisture simulations as well as forecasts of 2-meter air temperature and humidity and precipitation from NUWRF were observed using the NRT GVF data products. The RMSE in SM estimates from the off-line Noah model is reduced by around 1.0% (1.41%) during the green-up phase and by 1.48% (2.24%) over the senescence phase for the surface (root zone) SM simulations. Around 82.3% validation sites (out of 1178 sites) showed positive impact on coupled WRF model with the insertion of NRT GVF.

Impact of soil moisture initialization on boreal summer subseasonal forecasts: mid-latitude surface air temperature and heat wave events

This study uses a global land–atmosphere coupled model, the land–atmosphere component of the Global Seasonal Forecast System version 5, to quantify the degree to which soil moisture initialization could potentially enhance boreal summer surface air temperature forecast skill. Two sets of hindcast experiments are performed by prescribing the observed sea surface temperature as the boundary condition for a 15-year period (1996–2010). In one set of the hindcast experiments (noINIT), the initial soil moisture conditions are randomly taken from a long-term simulation. In the other set (INIT), the initial soil moisture conditions are taken from an observation-driven offline Land Surface Model (LSM) simulation. The soil moisture conditions from the offline LSM simulation are calibrated using the forecast model statistics to minimize the inconsistency between the LSM and the land–atmosphere coupled model in their mean and variability. Results show a higher boreal summer surface air temperature prediction skill in INIT than in noINIT, demonstrating the potential benefit from an accurate soil moisture initialization. The forecast skill enhancement appears especially in the areas in which the evaporative fraction—the ratio of surface latent heat flux to net surface incoming radiation—is sensitive to soil moisture amount. These areas lie in the transitional regime between humid and arid climates. Examination of the extreme 2003 European and 2010 Russian heat wave events reveal that the regionally anomalous soil moisture conditions during the events played an important role in maintaining the stationary circulation anomalies, especially those near the surface.

Pengaruh Korelasi Data pada Peramalan Suhu Udara Menggunakan Backpropagation Neural Network

Air temperature forecasting is important role in agriculture, flight, trading and so on. The method used for forecasting is Neural Network (NN). NN works as human neural system. One of NN type used for forecasting is Backpropagation where Backpropagation model is there are hidden layers between input and output. Due to forecasting result depends on data correlation, then this research will explain about the effect of data correlation on air temperature forecasting. To obtain forecasting result, Backpropagation algorithm will be used. Simulations are applied in three dataset with different structures. Based on simulation results, data which have strong correlation can result better forecasting based on smaller Mean Square Error (MSE).

Bias correction to improve the skill of summer precipitation forecasts over the contiguous United States by the North American multi‐model ensemble system

Improvements in skill of summer forecasted precipitation as produced by the North American multi‐model ensemble (NMME) system over the contiguous United States (CONUS) are examined by applying a new bias correction method. The uncorrected precipitation produced by NMME hindcasts exhibits good prediction skill in fall and winter, while the spring and summer forecasts are marked with extremely poor skill. We propose a new method to correct the forecasted precipitation distribution based on skillfully predicted 2‐m air temperature (T2m) forecasts to fully exploit the stronger co‐variability that exists between precipitation and T2m in nature. The occurrence of enhanced recycled precipitation over CONUS provides an ideal situation to hone precipitation forecast skills using the T2m forecasts. The proposed bias correction is shown to successfully reduce the root mean square error in precipitation hindcasts in summer and can easily be extended to real‐time forecasts, thus providing a framework to dynamically link precipitation with other predictors besides T2m. Process understanding of the observed T2m‐precipitation relation will offer a framework for diagnosing poor model skill.