Quality of the Wind Wave Forecast in the Black Sea Including Storm Wave Analysis

This paper presents the results of wind wave forecasts for the Black Sea. Three different versions utilized were utilized: the WAVEWATCH III model with GFS 0.25 forcing on a regular grid, the WAVEWATCH III model with COSMO-RU07 forcing on a regular grid, and the SWAN model with COSMO-RU07 forcing on an unstructured grid. AltiKa satellite altimeter data were used to assess the quality of wind and wave forecasts for the period from 1 April to 31 December 2017. Wave height and wind speed forecast data were obtained with a lead time of up to 72 h. The presented models provide an adequate forecast in terms of modern wave modeling (a correlation coefficient of 0.8–0.9 and an RMSE of 0.25–0.3 m) when all statistics were analyzed. A clear improvement in the wave forecast quality with the high-resolution wind forecast COSMO-RU07 was not registered. The bias error did not exceed 0.5 m in an SWH range from 0 to 3 m. However, the bias sharply increased to −2 or −3 m for an SWH range of 3–4 m. Wave forecast quality assessments were conducted for several storm cases.

Verification of Forecast Performance of a Rapid Refresh Wave Model Based on Wind–Wave Interaction Effect

In this study, we constructed a rapid refresh wave forecast model using sea winds from the Korea Local Analysis and Prediction System as input forcing data. The model evaluated the changes in forecast performance considering the influence of input wind–wave interaction, which is an important factor that determines forecast performance. The forecast performance was evaluated by comparing the forecast results of the wave model with the significant wave height, wave period, and wave direction provided by moored buoy observations. During the typhoon season, the model tended to underestimate the conditions, and the root mean square error (RMSE) was reduced by increasing the wind and wave interaction parameter. The best value of the interaction parameter that minimizes the RMSE was determined based on the results of the numerical experiments performed during the typhoon season. The forecast error in the typhoon season was higher than that observed in the analysis results of the non-typhoon season. This can be attributed to the variations of the wave energy caused by the relatively strong typhoon wind field considered in the wave model.

Anticipation Mechanism for Cold Wave: Forecast Based Financing a Case Study in the Peruvian Andes

In June 2018, the Peruvian provinces of Arequipa and Puno in the southern Andean region were affected by heavy snowfall, which caused severe damage to people and livelihoods in several communities. Using the Forecast-based Financing approach, the Peruvian Red Cross implemented its pre-defined early action protocol before this event, after receiving an extreme snowfall warning (Level 4) from the Peruvian meteorological service. Here, we provide a case study of the approach and event itself, documenting the decision-making thresholds as well as the actions taken. This warning activated the thresholds established in the protocol, and Peruvian Red Cross prioritized 10 communities for pre-disaster support based on the forecasted severity of the event in combination with vulnerability and exposure information. The activation took place 2 days before the extreme snowfall in the communities, and the Red Cross distributed veterinary kits for 50 heads of cattle per family, tarpaulins, tool kits to install a temporary awning for alpacas to protect them from the cold wind and snow, protection kits for alpaca herders and warm clothing for children under five, pregnant women, the elderly, and people with chronic and infectious diseases in 430 highly vulnerable households. This article presents the results of a household survey following the impact of the extreme snowfall. We document the early actions taken by these communities to protect their livelihoods, health, and assets. The evaluation also presents descriptive statistics of household-level outcomes for households receiving pre-snowfall support and those that did not receive any intervention or only received post-disaster assistance. While most households took action to protect their assets, there were fewer extreme losses of alpaca herds reported in the communities who received the early support, and these communities also reported fewer adults suffering from respiratory illnesses. More in-depth research on this type of early action is necessary on a wider scale, especially to evaluate the utility of different support measures and the necessary quantity of support needed. This case study can inform government, civil society, and humanitarian actors of how early action is happening before disasters occur and provide a direction for further investment in research and practice to make use of hydro-meteorological forecasts for the benefit of the most vulnerable.

Evaluating skill of BMKG wave model forecast (Wavewatch-3) with observation data in Indian Ocean (5 – 31 December 2017).

Providing Maritime meteorological forecasts (including ocean wave information) is one of BMKG duties. Currently, BMKG employs Wavewatch-3 (WW3) model to forecast ocean waves in Indonesia. Evaluating the wave forecasts is very important to improve the forecasts skill. This paper presents the evaluation of 7-days ahead BMKG’s wave forecast. The evaluation was performed by comparing wave data observation and BMKG wave forecast. The observation data were obtained from RV Mirai 1708 cruise on December 5th to 31st 2017 at the Indian Ocean around 04°14'S and 101°31'E. Some statistical properties and Relative Operating Characteristics (ROC) curve were utilized to assess the model performance. The evaluation processes were carried out on model’s parameters: Significant Wave Height (Hs) and Wind surface for each 7-days forecast started from 00 UTC. The comparation results show that, in average, WW3 forecasts are over-estimate the wave height than that of the observation. The forecast skills determined from the correlation and ROC curves are good for the first- and second-day forecast, while the third until seventh day decrease to fair. This phenomenon is suspected to be caused by the wind data characteristics provided by the Global Forecasts System (GFS) as the input of the model. Nevertheless, although statistical correlation is good for up to 2 days forecast, the average value of Root Mean Square Error (RMSE), absolute bias, and relative error are high. In general, this verifies the overestimate results of the model output and should be taken into consideration to improve BMKG’s wave model performance and forecast accuracy.

Updated System for the Sea Wave Operational Forecast of the Black Sea Marine Forecasting Center

Purpose. The work is aimed at updating the sea wave forecasting system developed in the Black Sea Marine Forecasting Center by including the block of wind wave forecast in the Sevastopol region and by improving the wave forecast accuracy using the proposed procedure for the SWAN model tuning. Methods and Results. In the updated forecasting system, the possibility of performing the joint operational sea wave forecasts for the Black Sea and the Sevastopol region (with the 5 and 1 km spatial resolutions, respectively) became possible due to the nested grid method applied. To improve accuracy of the wave forecasts, the procedure for the SWAN model tuning was proposed. It is based on changing the parameterization of the surface friction coefficient Cd(V), where V is the surface wind speed. This permits to reduce the deviations of the forecasted wave heights from those obtained from the satellite altimetry measurements. Efficiency of the proposed procedure was assessed through comparison of the forecasting results with the remote sensing data. It is shown that in the forecasts supplied with an optimal choice of functional dependence Cd(V), the scattering index between the forecasted and measured values can be reduced by 20 %. Conclusions. Represented is the updated system of the Black Sea Marine Forecasting Center intended for the joint operational sea wave forecasts in the Black Sea and in the Sevastopol region. The results of model validation have shown that the procedure proposed for tuning the SWAN model makes it possible to reduce the deviations of the forecasted wave heights from those measured by the sensors installed at the altimetry satellites.

Advancing the Performance of Wave Forecast Models Under Low Wind Conditions

Ocean surface is complex and difficult to predict accurately due to its random nature. Ocean surface waves in strong wind conditions have been widely studied for last few decades. Almost half of world’s winds are below 7.5 m/s and the physics of such winds contains a lot of uncertainties. The simulation of ocean waves is largely dependent on the driving winds force accuracy and source term parameterizations. However, low winds are often ignored on the perception of their lesser effect on overall results of existing models. It is important to understand the relative strength/ weaknesses of wave forecast models under low wind conditions from scientific perspective which should lead to improved wave forecast and wave-ocean-weather coupling capabilities. There are many critical thresholds involved in the initial generation and growth of wind waves whereas current parameterizations of wave models are mostly based on moderate – high wind conditions. Wave model’s performance, although not very prominent, contains bias under low winds conditions and these thresholds need to be embedded in current physics of wave forecast models for more accurate simulations. In this study, WAVEWATCH III (v6.07) wave forecast model with observation based source terms parameterizations (ST6 package) is used to simulate waves on a global scale. The model’s output is analyzed with a globally calibrated and cross validated global dataset of 13 altimeters to analyze its performance under low wind conditions. A relative error of −1 to 6 is observed in global significant wave heights simulated by WAVEWATCH III model compared to altimeter’s measured wave heights for wind speeds less than 5ms−1.

Evaluating sub-seasonal heatwave reforecasts of the ECMWF over Europe

<p>Severe heatwaves have harmful impacts on ecosystems and society. Early warning of heat waves help with decreasing their harmful impact. Previous research shows that the Extended Range Forecasts (ERF) of the European Centre for Medium-Range Weather Forecasts (ECMWF) have over Europe a somewhat higher reforecast skill for extreme hot summer temperatures than for long-term mean temperatures. Also it has been shown that the reforecast skill of the ERFs of the ECMWF was strongly increased by the most severe heat waves (the European heatwave 2003 and the Russian heatwave 2010).</p><p>Our aim is to be able to estimate the skill of a heat wave forecast at the time the forecast is given. For that we investigated the spatial and temporal reforecast skill of the ERFs of the ECMWF to forecast hot days (here defined as a day on which the 5 days running mean surface temperature is above its summer 90<sup>th</sup> percentile) in the continental Europe in summers 2000-2019. We used the ECMWF 2-meter temperature reforecasts and verified them against the ERA5 reanalysis. The skill of the hot day reforecasts was estimated by the symmetric extremal dependence index (SEDI) which considers both hit rates and false alarm rates of the hot day forecasts. Further, we investigated the skill of the heatwave reforecasts based on at which time steps of the forecast the hot days were forecasted. We found that on the mesoscale (horizontal scale of ~500 km) the ERFs of the ECMWF were most skillful in predicting the life cycle of a heat wave (lasting up to 25 days) about a week before its start and during its course. That is, on the mesoscale those reforecasts, in which hot day(s) were forecasted to occur during the first 7…11 days, were more skillful on lead times up to 25 days than the rest of the heat wave forecasts. This finding is valuable information, e.g., in the energy and health sectors while preparing for a coming heat wave.</p><p>The work presented here is part of the research project HEATCLIM (Heat and health in the changing climate) funded by the Academy of Finland.</p>

ConvLSTM-Based Wave Forecasts in the South and East China Seas

Numerical wave models have been developed for the wave forecast in last two decades; however, it faces challenges in terms of the requirement of large computing resources and improvement of accuracy. Based on a convolutional long short-term memory (ConvLSTM) algorithm, this paper establishes a two-dimensional (2D) significant wave height (SWH) prediction model for the South and East China Seas trained by WaveWatch III (WW3) reanalysis data. We conduct 24-h predictions under normal and extreme conditions, respectively. Under the normal wave condition, for 6-, 12-, and 24-h forecasting, their correlation coefficients are 0.98, 0.93, and 0.83, and the mean absolute percentage errors are 15, 29, and 61%. Under the extreme condition (typhoon), for 6 and 12 h, their correlation coefficients are 0.98 and 0.94, and the mean absolute percentage errors are 19 and 40%, which is better than the model trained by all the data. It is concluded that the ConvLSTM can be applied to the 2D wave forecast with high accuracy and efficiency.