Strength and Challenges of global model MPAS with regional mesh refinement for mid-latitude storm forecasting: a case study

With the rising share of renewable energy sources like wind energy in the energy mix, high-impact weather events like mid-latitude storms increasingly affect energy production, grid stability and safety and reliable forecasting becomes very relevant for e.g. transmission system operators to allow for actions to reduce imbalances. Traditionally, meteorological forecasts are provided by limited-area weather prediction models (LAMs), which can use high enough model resolution to represent the range of atmospheric scales of motions associated with such storm structures. While generally satisfactory, deterioration and insufficient deepening of large-scale storm structures are observed when they are introduced near the lateral boundaries of the LAM due to inadequate spatial and temporal interpolation. Global models with regional mesh refinement capabilities like the Model for Prediction Across Scales (MPAS) have the potential to provide an alternative, while avoiding sharp resolution jumps and lateral boundaries. In this study, MPAS' capabilities of simulating key evaluation metrics like storm intensity, storm location and storm duration are investigated based on a case study and assessed in comparison with buoy measurements, forecast products from the Climate Forecast System (CFSv2) and simulations with the Weather Research and Forecasting (WRF) LAM. Quasi-uniform and variable-resolution MPAS mesh configurations with different model physics settings are designed to analyze the impact of the mesh refinement and model physics on the model performance. MPAS shows good performance in predicting storm intensity based on the local minimum sea level pressure, while time of local minimum sea level pressure (storm duration) was generally estimated too late (too long) in comparison with the buoy measurements in part due to an early west-wards shift of the storm center in MPAS. The variable-resolution configurations showed a combination of an additional south-westwards shift and deviations in the sea level pressure field south-west of the storm center that introduced additional bias to the time of local minimum sea level pressure at some locations. The study highlights the need for a more detailed analysis of applied mesh refinements for particular applications and emphasizes the importance of methods like data assimilation techniques to prevent model drifts.

Wave Measurements Using Multi-Frame Processing of Marine Radar Data

Marine radars have proven to be useful for measuring ocean waves, but the accuracy of the measurements is limited by several factors including the look-angle dependence of the radar signals as well as noise in the radar data. The look-angle dependence introduces a systematic error or bias in the measurements, and noise causes a random error. This paper describes a method of combining data from multiple radar frames that is optimal in the sense of minimizing the error for a set of biased measurements with random additive noise. The results are shown experimentally to increase the correlation of the radar estimates with buoy measurements.

Assessment of Global FY-3C/VIRR Sea Surface Temperature

Fengyun-3C (FY-3C) is a second-generation meteorological satellite of China that was launched on 23 September 2013. The on board Visible and Infrared Radiometer (VIRR) can be used to observe global sea surface temperature (SST). In this paper, the VIRR SST products are compared with MODIS SST products and buoy measurements from 2015 to 2019. The collocations of VIRR, MODIS, and buoy SST are generated separately during the day and night with the spatial window of 0.05° × 0.05°. The comparison results show that the biases of VIRR SST minus buoy SST during the day and night are −0.21 and −0.13 °C with a corresponding robust standard deviation (RSD) of 0.58 and 0.59 °C, respectively. The mean differences between VIRR and MODIS are −0.10 and 0.08 °C with RSDs of 0.53 and 0.58 °C for the daytime and nighttime, respectively. The consistency of VIRR SST accuracy from 2015 to 2019 and the dependence of VIRR SST error on SST and latitude are also investigated.

Sea State Estimation from Uncalibrated, Monoscopic Video

Video of the ocean surface is used as a means for estimating the sea state. Time series of pixel intensity values are given as input to a method that uses the Kalman filter and the least squares approximate solution for estimating the uncalibrated video amplitude spectrum. A method is proposed for scaling this spectrum to metres with the use of an empirical model of the ocean. The significant wave height is estimated from the calibrated video amplitude spectrum. The results are tested against two sets of video data, and buoy measurements in both cases are solely used for indicating the true state. For significant wave height values between 0.5 and 3.6 m, the maximum observed value of root mean square error is 0.37 m and of mean absolute percentage error 16%.

Using Saildrones to Validate Satellite-Derived Sea Surface Salinity and Sea Surface Temperature along the California/Baja Coast

Traditional ways of validating satellite-derived sea surface temperature (SST) and sea surface salinity (SSS) products by comparing with buoy measurements, do not allow for evaluating the impact of mesoscale-to-submesoscale variability. We present the validation of remotely sensed SST and SSS data against the unmanned surface vehicle (USV)—called Saildrone—measurements from the 60 day 2018 Baja California campaign. More specifically, biases and root mean square differences (RMSDs) were calculated between USV-derived SST and SSS values, and six satellite-derived SST (MUR, OSTIA, CMC, K10, REMSS, and DMI) and three SSS (JPLSMAP, RSS40, RSS70) products. Biases between the USV SST and OSTIA/CMC/DMI were approximately zero, while MUR showed a bias of 0.3 °C. The OSTIA showed the smallest RMSD of 0.39 °C, while DMI had the largest RMSD of 0.5 °C. An RMSD of 0.4 °C between Saildrone SST and the satellite-derived products could be explained by the diurnal and sub-daily variability in USV SST, which currently cannot be resolved by remote sensing measurements. SSS showed fresh biases of 0.1 PSU for JPLSMAP and 0.2 PSU and 0.3 PSU for RMSS40 and RSS70 respectively. SST and SSS showed peaks in coherence at 100 km, most likely associated with the variability of the California Current System.