43results about How to "Improve the forecast effect" patented technology

The invention discloses a wind speed forecasting method based on fine boundary layer figure mode for a wind farm in complex terrain. The method comprises the following steps: acquiring historical wind measuring tower data; converting acquired terrain data of a geological information system and the like into static data which can be directly called by a mesoscale weather forecast mode and the fine boundary layer figure mode; carrying out wind farm localization configuration for the mesoscale weather forecast mode and the fine boundary layer figure mode, so as to achieve optimal mode meteorological environment simulation; and taking the mesoscale weather forecast mode and the fine boundary layer figure mode as main body to build a wind farm wind speed forecasting system, and carrying out dynamically adjustable wind farm wind speed forecasting for areas 500 square kilometers around the wind farm for 3-7 days, wherein the horizontal grid resolution is 100m, and the time interval is 5-15 minutes. According to the invention, as the fine terrain data is introduced, and the fine boundary layer figure mode is adopted for100m-resolution dynamic downscaling forecasting. Therefore, the method is more suitable for wind farm wind speed forecasting under complex terrain conditions.

An atmospheric heavy pollution forecast method based on a combination of a numerical model and a statistic analysis includes that: national centers for environmental prediction (NCEP) global forecast ambient field data are obtained; a forecast trigger command is compulsively generated by manual power or automatically generated after the operation of a meteorological model finishes; a forecast command is started to obtain a meteorological factor data set of a simulation area and surrounding areas; air quality monitoring data are obtained; a forecast factor set data file is generated; the visibility of a forecast day is obtained through a visibility forecast sub-mode; an air quality level of the forecast day is differentiated in a qualitative mode through a pollution level initial differentiation sub-mode; a weather type of the forecast day is diagnosed and identified through a weather type identification sub-mode; pollutant concentration of the forecast day is calculated through a heavy pollution quantitative forecast sub-mode; a hazard level of the pollution level to a human body is confirmed, and administrators are provided with a decision basis for emergency management. Compared with a high concentration pollution weather forecast effect of existing air quality forecast systems at home and abroad, the atmospheric heavy pollution forecast method based on the combination of the numerical model and the statistic analysis remarkably improves the air pollution forecast effect.

The invention provides an engine oil filter element abrasive dust detector, comprising a filter element cleaning barrel, a particle counter, an abrasive dust membrane making device, an oil tank and a control system; wherein the filter element cleaning barrel, the particle counter, the abrasive dust membrane making device and the oil tank are sequentially connected into a circulating system by an oil pipeline; and the control system is respectively connected the other devices, so as to control the other devices. The detector can realize automatic cleaning on filter element, abrasive dust size and quantitative distribution analysis and abrasive dust membrane making, large particle metal abrasive dust in machine lubricating system is monitored, forecasting capacity of machine abrasion fault is improved, and the defects in the prior art are overcome.

The invention brings forward a constructing method of a combined air quality forecasting model, wherein the method is based on a BP neural network and multi-element stepwise regression. The method comprises the following steps: (1), establishing a BP neural network forecasting module based on a training sample set; (2), carrying out severe-pollution scene determination based on the BP neural network forecasting result; to be specific, (21), defining a severe-pollution scene; (22), establishing a determination equation; (23), carrying out determination by using a neural network forecasting value; and (24), carrying out determination on the determination equation based on the neural network forecasting value determination result; (3), establishing a severe-pollution multi-element stepwise regression forecasting model according to the severe-pollution scene determination result; and (4), with combination of the forecasting determination process, outputting a forecasting result. According to the invention, the forecasting precision of the urban air quality, especially the early-warning forecasting of the severe-pollution scene, is improved comprehensively; and thus stable air quality precision forecasting under different pollution degrees can be realized.

The present invention provides a numerical forecast product anomaly integral dynamic statistical correction method and system, comprising: step 1, data preprocessing process; step 2, climate average state extraction process; step 3, physical decomposition process; step 4, correction process: Using ERA‑Interim reanalysis data to correct the circulation field results of ECMWF medium-range numerical forecast products, and using hourly precipitation datasets to correct the precipitation results of ECMWF medium-range numerical forecast products. The present invention provides a numerical forecast product anomaly integral dynamic statistical correction method and system, which is a numerical forecast correction method and system that can effectively reduce the systematic error of ECMWF medium-term numerical forecast products, significantly improve its forecast accuracy and available forecast timeliness The revised system can effectively improve the forecasting ability of the product in the short-term-extended period, heavy precipitation and other severe weather systems.