Snowfall model validation using surface observations and an optimal estimation snowfall retrieval

In the winter, orographic precipitation falls as snow in the mid to high latitudes where it causes avalanches, affects local infrastructure, or leads to flooding during the spring thaw. We present a technique to validate operational numerical weather prediction model simulations in complex terrain. The presented verification technique uses a combined retrieval approach to obtain surface snowfall accumulation and vertical profiles of snow water at the Haukeliseter test site, Norway. Both surface observations and vertical profiles of snow are used to validate model simulations from the Norwegian Meteorological Institute’s operational forecast system and two simulations with adjusted cloud microphysics.Retrieved surface snowfall is validated against measurements conducted with a double-fence automated reference gauge (DFAR). In comparison, the optimal estimation snowfall retrieval produces + 10.9% more surface snowfall than the DFAR. The predicted surface snowfall from the operational forecast model and two additional simulations with microphysical adjustments (CTRL and ICE-T) are overestimated at the surface with +41.0 %, +43.8 %, and +59.2 %, respectively. Simultaneously, the CTRL and ICE-T simulations underestimate the mean snow water path by -1071.4% and -523.7 %, respectively.The study shows that we would reach false conclusions only using surface accumulation or vertical snow water content profiles. These results highlight the need to combine ground-based in-situ and vertically-profiling remote sensing instruments to identify biases in numerical weather prediction.

New operational nowcasting system at Finnish Meteorological Institute

<p>Rapidly updating nowcasting system, Smartmet nowcast, has been developed at Finnish Meteorological Institute (FMI). The system combines information from multiple sources to operationally produce accurate and timely short range forecasts and a detailed description of the present weather to the end-users. The information sources combined are 1) Rapidly-updating high-resolution numerical weather prediction (NWP) MetCoOp nowcast (MNWC) forecast 2) radar-based nowcast 3) 10-day operational forecast. The Smartmet nowcast is currently produced for parameters 2-m temperature, 10-m wind speed, relative humidity, total cloud cover and accumulated 1-hour precipitation.</p><p>The system produces hourly updating nowcast information over the Scandinavian forecast domain and combines it seamlessly with the 10-day operational forecast information. Prior the combination a simple bias correction scheme based on recent forecast error information is applied to MNWC model analysis and forecast fields of 2-m temperature, relative humidity and 10-m wind speed. The blending of the nowcast and the 10-day operational forecast information is done using Optical-flow based image morphing method, which provides visually seamless forecasts for each forecast variable.</p><p>FMI has operationally produced Smartmet nowcast forecasts since September 2020. The validation of the data is in progress. The available results show that the Smartmet nowcast is improving the quality of short range forecasts and producing seamless and consistent forecasts. The method is also reducing the delay of forecast production. The Smartmet nowcast method will be automated in FMI forecast production in the near future.</p>

Low-flow forecasting in France: update on the latest developments of the PREMHYCE operational forecast platform

<p>Low-flow forecasting can help to improve water management at places where a number of uses can be affected by diminishing water supply from rivers. Several French institutes (INRAE, BRGM, EDF, Lorraine University and Météo-France) have been collaborating to set up an operational platform, called PREMHYCE, for low-flow forecasting at the national scale, in cooperation with operational services. PREMHYCE includes five hydrological models and low-flow forecasts can be issued up to 90 days ahead for more than 800 basins. Several input scenarios are considered: ECMWF 14-days ensemble forecasts, ensemble streamflow prediction (ESP) using historical climatic data, and a no precipitation scenario. Outputs from the different hydrological models are combined into a multi-model approach to improve robustness of the forecasts. The tool provides text files and graphical representation of forecasted low-flows, as well as key low-flow indicators, such as the probabilities of being under low-flow thresholds provided by operational services. The presentation will show the main characteristics of this operational forecast platform, its latest developments and the results on the recent low-flow periods.</p>

Development of a MetUM (v 11.1) and NEMO (v 3.6) coupled operational forecast model for the Maritime Continent – Part 1: Evaluation of ocean forecasts

This article describes the development and ocean forecast evaluation of an atmosphere–ocean coupled prediction system for the Maritime Continent (MC) domain, which includes the eastern Indian and western Pacific oceans. The coupled system comprises regional configurations of the atmospheric model MetUM and ocean model NEMO at a uniform horizontal resolution of 4.5 km × 4.5 km, coupled using the OASIS3-MCT libraries. The coupled model is run as a pre-operational forecast system from 1 to 31 October 2019. Hindcast simulations performed for the period 1 January 2014 to 30 September 2019, using the stand-alone ocean configuration, provided the initial condition to the coupled ocean model. This paper details the evaluations of ocean-only model hindcast and 6 d coupled ocean forecast simulations. Direct comparison of sea surface temperature (SST) and sea surface height (SSH) with analysis, as well as in situ observations, is performed for the ocean-only hindcast evaluation. For the evaluation of coupled ocean model, comparisons of ocean forecast for different forecast lead times with SST analysis and in situ observations of SSH, temperature, and salinity have been performed. Overall, the model forecast deviation of SST, SSH, and subsurface temperature and salinity fields relative to observation is within acceptable error limits of operational forecast models. Typical runtimes of the daily forecast simulations are found to be suitable for the operational forecast applications.

Forecasting the Spatial Behavior of a Forest Fire at Uncertainty and Instability of the Process

The Russian forest fund, being a public domain of the people and a special kind of federal property, requires sustainable management at the national level. One of the key principles of forest management is to ensure that forests are conserved and protected against a wide range of threats, primarily forest fires. Although forest fires are a natural component of forest ecosystems and cannot be completely eliminated, researchers have currently revealed a decrease in the regulatory function and an increase in the destructive function of forest fires. Understanding the interrelations between the environmental factors and forest fire history is necessary for the development of effective and scientifically sound forest safety plans. The main purpose of the study is to increase the efficiency of the formation of an operational forecast of a forest fire in difficult conditions of a real fire (at instability and uncertainty). The author analyzed statistical data on forest fires the USA, Canada, Russia and the five southern European Union member states (Portugal, Spain, France, Italy and Greece) and confirmed the conclusion on the increase in the frequency of large forest fires. The most widely used in practice forecasting models of forest fire dynamics (Van Wagner, Rothermel, Finney, Cruz, etc.) and their computer implementations (Prometheus, FlamMap, FARSITE, VISUAL-SEVEIF, WILDFIRE ANALYST) are presented in the article. It is proposed to develop an intelligent system designed to create an operational forecast of a forest fire using convolutional neural networks (CNN). The structure of this system is described. It includes three main subsystems: information, intelligent and user interface. A key element of the intelligent subsystem is a forest fire propagation model, which recognizes data from sequential images, predicts the forest fire dynamics, and generates an image with a fire spread forecast. The scheme of the proposed model is described. It includes the following stages: data input; preprocessing of input data; recognition of objects using CNNs; forecasting the forest fire dynamics; output of operational forecast. The implementation features of the stage “recognition of objects using CNNs” are presented in detail: core size for each convolutional layer 3×3, activation function ReLu(x), filter in 2×2 pooling layers with step 2, max-pooling method, Object recognition and Semantic segmentation methods at the networks output.