Observing System Experiments with an Arctic Mesoscale Numerical Weather Prediction Model

Abstract

In the Arctic, weather forecasting is one element of risk mitigation, helping operators to have knowledge on weather-related risk in advance through forecasting capabilities at time ranges from a few hours to days ahead. The operational numerical weather prediction is an initial value problem where the forecast quality depends both on the quality of the forecast model itself and on the quality of the specified initial state. The initial states are regularly updated using environmental observations through data assimilation. This paper assesses the impact of observations, which are accessible through the global telecommunication and the EUMETCast dissemination systems on analyses and forecasts of an Arctic limited area AROME (Application of Research to Operations at Mesoscale) model (AROME-Arctic). An assessment through the computation of degrees of freedom for signals on the analysis, the utilization of an energy norm-based approach applied to the forecasts, verifications against observations, and a case study showed similar impacts of the studied observations on the AROME-Arctic analysis and forecast systems. The AROME-Arctic assimilation system showed a relatively high sensitivity to the humidity or humidity-sensitive observations. The more radiance data were assimilated, the lower was the estimated relative sensitivity of the assimilation system to different conventional observations. Data assimilation, at least for surface parameters, is needed to produce accurate forecasts from a few hours up to days ahead over the studied Arctic region. Upper-air conventional observations are not enough to improve the forecasting capability over the AROME-Arctic domain compared to those already produced by the ECMWF (European Centre for Medium-range Weather Forecast). Each added radiance data showed a relatively positive impact on the analyses and forecasts of the AROME-Arctic. The humidity-sensitive microwave (AMSU-B/MHS) radiances, assimilated together with the conventional observations and the Infrared Atmospheric Sounding Interferometer (IASI)-assimilated on top of conventional and microwave radiances produced enough accurate one-day-ahead forecasts of polar low.

An NWP Model Intercomparison of Surface Weather Parameters in the European Arctic during the Year of Polar Prediction Special Observing Period Northern Hemisphere 1

“…For a case study with a mesoscale low in the Barents Sea the new initial conditions improve the location of the mesoscale low by 140 km. Similar runs with initial surface conditions from AROME-Arctic in MF-AROME runs reduce the MF-AROME T2 errors to the same level as AROME-Arctic and highlight the importance of surface assimilation as also shown in Randriamampianina et al (2019) The forecast climatologies also reveal that there are differences that are not evaluated in this study due to the sparseness of observations. This includes differences over areas covered by sea ice (e.g., T2, TCC, and pre-cip24), ocean areas (TCC, precip24), and inland and mountain areas at Svalbard (e.g., WS10 and T2).…”

Supermodding – A special footprint operator for mesoscale data assimilation using scatterometer winds

“…Furthermore, the whole configuration and more details of the DA system are described by Randriamampianina et al . (2019).…”

“…For instance, the coverage of observations from polar‐orbiting satellites is one issue and the temporal availability of the remote‐sensing data is another in the verification procedure. Therefore, the verification against radiosonde observations was performed using only eight radiosondes, which are very well‐distributed inside the NWP model domain (Randriamampianina et al ., 2019). Common error statistics – the root mean square error (RMSE) and normalised differences in RMSE – are calculated by comparison of the model data to radiosondes and comparing different experiments.…”

“…For a given date, the numbers of active observations in the AROME-Arctic DA system are summarized in Table 1. Furthermore, the whole configuration and more details of the DA system are described by Randriamampianina et al (2019).…”

Spatiotemporal variability of agricultural drought and its association with climatic variables in the Upper Awash Basin, Ethiopia

“…In East Africa in general and in Ethiopia in particular, droughts have recurred in the last decades and is a major natural disaster that contributes to food insecurity and poverty [37][38][39]. In Ethiopia, the availability of drought has increased due to climate change and will cause a decline in water and agricultural production [40].…”