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

Abstract

Satellite observation footprints may extend over many grid points of a high-resolution limited-area model, while small and fast model scales may not be traceable by the observing system. We discuss the spatial representation of scatterometer ocean surface winds for the representation of high-resolution model state variables. A prototype observation operator called supermodding is studied in the variational assimilation framework in order to avoid correcting unconstrained small scales during the assimilation procedure. The challenges connected to small scales that are represented by the mesoscale model with respect to the application of the supermodding operator are discussed through idealised experiments. These results show that the application of the supermodding operator is able to avoid correcting unconstrained small scales, putting focus on the large scales only during data assimilation. Departure-based diagnostics show that the footprint representation helps to reduce the standard deviation of observation minus background departures (4-5% reduction) while the statistics for the supermodding method show a further reduction (8-11%). The impact of the supermodding approach is discussed through a forecast sensitivity study using a moist total energy norm (MTEN)-based technique and verification of the forecasts against observations. It is shown that the impact of the supermodding method to ASCAT data assimilation on the upper-air AROME-Arctic forecasts is observed over sea and near the surface, and that it is progressively shifted towards 700-800 hPa levels. Both supermodding at 30 km and at 60 km (i.e., twice the effective resolution of the applied scatterometer observations) show significant and consistent improvement on forecasts of lower tropospheric wind and temperature compared to the operational assimilation technique, as such demonstrating the robustness of the supermodding technique.

Operational assimilation of Aeolus winds in the Météo‐France global NWP model ARPEGE

“…Since the MCy data may be the only data available of useful quality at the end of the Aeolus mission, additional investigations will be carried out to evaluate separately the impact of the RCr and MCy channels with additional OSEs. In addition, possible evolutions of the observation operator could be considered in order to take into account the effect of the variable resolution of the ARPEGE model on the different scales of the Rayleigh and Mie data, by evaluating, for example, supermodding methods (Mile et al ., 2021).…”

Evaluating the use of Aeolus satellite observations in the regional numerical weather prediction (NWP) model Harmonie–Arome

“…Previous studies, for example Šavli et al (2018), evaluated the use of pre-launch test data in a 15 km LAM using the WRF model (Weather Research and Forecasting;Skamarock et al, 2008). We use the Harmonie-Arome model (Bengtsson et al, 2017) over the Nordic countries, covering the operational domain of MetCoOp (Meteorological Cooperation;Müller et al, 2017). The use of Aeolus observations in limited-area kilometre-scale data assimilation differs in several respects from global data assimilation.…”

Assimilation of Aeolus Rayleigh‐Clear Winds Using a Footprint Operator in AROME‐Arctic Mesoscale Model

“…This is the case for scatterometer observations in the AA system and the study of supermodding operator obtained a 8%–11% maximal reduction in the departure statistics (see Mile et al. (2021)). For Aeolus data, the averaging reduces the standard deviation of O‐B with a much smaller extent that is, around 1%.…”

“…Duffourg et al (2010) employed footprint operator for infrared radiances and Kleespies (2009) aimed to utilize aggregated surface information for microwave radiances. Lately, a similar problem with respect to satellite representativeness was studied by Marseille and Stoffelen (2017) with an idealized framework and with a real NWP simulations by Mile et al (2021) focusing on ocean surface satellite winds from scatterometer data. These scatterometer studies explain that the small scales of high-resolution NWP models are generally unconstrained because the system is lacking physical forcing and enough observations over the open ocean.…”

“…These scatterometer studies explain that the small scales of high-resolution NWP models are generally unconstrained because the system is lacking physical forcing and enough observations over the open ocean. Therefore, a special footprint operator is needed which is called supermodding (Mile et al, 2021). The challenges of high-resolution data assimilation and the objective of the supermodding method are comprehensively motivated by Stoffelen et al (2020).…”