Using the Residual Network Module to Correct the Sub-Seasonal High Temperature Forecast

The high temperature forecast of the sub-season is a severe challenge. Currently, the residual structure has achieved good results in the field of computer vision attributed to the excellent feature extraction ability. However, it has not been introduced in the domain of sub-seasonal forecasting. Here, we develop multi-module daily deterministic and probabilistic forecast models by the residual structure and finally establish a complete set of sub-seasonal high temperature forecasting system in the eastern part of China. The experimental results indicate that our method is effective and outperforms the European hindcast results in all aspects: absolute error, anomaly correlation coefficient, and other indicators are optimized by 8–50%, and the equitable threat score is improved by up to 400%. We conclude that the residual network has a sharper insight into the high temperature in sub-seasonal high temperature forecasting compared to traditional methods and convolutional networks, thus enabling more effective early warnings of extreme high temperature weather.

Method of Air Temperature Forecast in Mechanized Longwall Workings in the Conditions of Vietnamese Mines

In the most recent years, the Vietnam National Coal - Mineral Industries Holding CorporationLimited (VINACOMIN) has been dynamically developing mechanization technologies in undergroundcoal mines. The climatic conditions of Vietnam, as well as increasing the depth of the coal seams and theproduction capacity, contribute to an air temperature increasing in mining excavations. The articlepresents statistical equations enabling air temperature forecasting at the outlet of mechanized longwallworkings. The results of numerical calculations, obtained from the solutions of the adopted mathematicaldescriptions, were compared with the measurement results and the statistical significance of the obtaineddeviations was determined. The performed analysis allowed to assess the practical usefulness of theadopted model for the air temperature forecasting in the workings of mechanized underground mines inVietnam. The presented method can be used as a tool for mining services in the fight against the climatethreat in underground excavations.

Evaluation of bias free rainfall forecasts and Kalman filtered temperature forecasts of T-80 model over Indian monsoon region

A forecasting system for obtaining objective medium range location specific forecast of surface weather elements is evolved at National Centre for Medium Range Weather Forecasting (NCMRWF). The basic information used for this is the output from the general circulation models (GCMs) T-80/T-254 operational at NCMRWF. The most essential component of the system is Direct Model Output (DMO) forecast. This is explained in brief. Direct Model Output (DMO) forecast is obtained from the predicted surface weather elements from the GCM. The two important weather parameters considered in detail are rainfall and temperature. Both the weather parameters have biases. While the bias from the rainfall is reduced by adopting bias removal technique based upon threshold values for rainfall and for removing bias from temperature forecast a two parameter Kalman filter is applied. The techniques used for getting bias free forecast are explained in detail. Finally an evaluation of the forecast skill for the Kalman filtered temperature forecast and bias free rainfall forecast during monsoon 2007 is presented.

The “Seili-index” For The Prediction of Chlorophyll-α Levels In The Archipelago Sea of The Northern Baltic Sea, Southwest Finland

To build a forecasting tool for the state of eutrophication in the Archipelago Sea, we fitted a Generalized Additive Mixed Model (GAMM) to marine environmental monitoring data, which were collected over the years 2011-2019 by an automated profiling buoy at the Seili ODAS-station. The resulting “Seili-index” can be used to predict the chlorophyll-α (chl-a) concentration in the seawater a number of days ahead by using the temperature forecast as a covariate. An array of test predictions with two separate models on the 2019 data set showed that the index is adept at predicting the amount of chl-a especially in the upper water layer. The visualization with 10 days of chl-a level predictions are presented online at https://saaristomeri.utu.fi/seili-index/. We also applied GAMMs to predict abrupt blooms of cyanobacteria on the basis of temperature and wind conditions and found the model to be feasible for short-term predictions. The use of automated monitoring data and the presented GAMM model in assessing the effects of natural resource management and pollution risks is discussed.

Asymmetry in Subseasonal Surface Air Temperature Forecast Error with Respect to Soil Moisture Initialization

Soil moisture (W) helps control evapotranspiration (ET), and ET variations can in turn have a distinct impact on 2-m air temperature (T2M), given that increases in evaporative cooling encourage reduced temperatures. Soil moisture is accordingly linked to T2M, and realistic soil moisture initialization has, in previous studies, been shown to improve the skill of subseasonal T2M forecasts. The relationship between soil moisture and evapotranspiration, however, is distinctly nonlinear, with ET tending to increase with soil moisture in drier conditions and to be insensitive to soil moisture variations in wetter conditions. Here, through an extensive analysis of subseasonal forecasts produced with a state-of-the-art seasonal forecast system, this nonlinearity is shown to imprint itself on T2M forecast error in the conterminous United States in two unique ways: (i) the T2M forecast bias (relative to independent observations) induced by a negative precipitation bias tends to be larger for dry initializations, and (ii) on average, the unbiased root-mean-square error (ubRMSE) tends to be larger for dry initializations. Such findings can aid in the identification of forecasts of opportunity; taken a step further, they suggest a pathway for improving bias correction and uncertainty estimation in subseasonal T2M forecasts by conditioning each on initial soil moisture state.

Accounting for skew when post-processing MOGREPS-UK temperature forecast fields

When statistically post-processing temperature forecasts, it is almost always assumed that the future temperature follows a Gaussian distribution conditional on the output of an ensemble prediction system. Recent studies, however, have demonstrated that it can at times be beneficial to employ alternative parametric families when post-processing temperature forecasts, that are either asymmetric or heavier-tailed than the normal distribution. In this article, we compare choices of the parametric distribution used within the Ensemble Model Output Statistics (EMOS) framework to statistically post-process 2m temperature forecast fields generated by the Met Office’s regional, convection-permitting ensemble prediction system, MOGREPS-UK. Specifically, we study the normal, logistic and skew-logistic distributions. A flexible alternative is also introduced that first applies a Yeo-Johnson transformation to the temperature forecasts prior to post-processing, so that they more readily conform to the assumptions made by established post-processing methods. It is found that accounting for the skewness of temperature when post-processing can enhance the performance of the resulting forecast field, particularly during summer and winter and in mountainous regions.