Comparison of temperature and wind observations in the Tropics in a perfect‐model, global EnKF data assimilation system

Abstract

Flow‐dependent errors in tropical analyses and short‐range forecasts are analysed using global observing‐system simulation experiments assimilating only temperature, only winds, and both data types using the ensemble Kalman filter (EnKF) Data Assimilation Research Testbed (DART) and a perfect model framework. The idealised, homogeneous observation network provides profiles of wind and temperature data from the nature run for January 2018 using the National Center for Atmospheric Research (NCAR) Community Earth System Model (CESM) forced by the observed sea‐surface temperature. The results show that the assimilation of abundant wind observations in a perfect model makes the temperature data in the Tropics largely uninformative. Furthermore, the assimilation of wind data reduces the background errors in specific humidity twice as much as the assimilation of temperature observations. In all experiments, the largest analysis uncertainties and the largest short‐term forecast errors are found in regions of strong vertical and longitudinal gradients in the background wind, especially in the upper troposphere and lower stratosphere over the Indian Ocean and Maritime Continent. The horizontal error correlation scales are on average short throughout the troposphere, just several hundred km. The correlation scales of the wind variables in precipitating regions are half of those in nonprecipitating regions. In precipitating regions, the correlations are elongated vertically, especially for the wind variables. Strong positive cross‐correlations between temperature and specific humidity in the precipitating regions are explained using the Clausius–Clapeyron equation.

WRF-PDAF v1.0: implementation and application of an online localized ensemble data assimilation framework

“…The LESTKF has found application across diverse studies, encompassing the assimilation of satellite data into atmosphere models (Mingari et al, 2022), ocean models (Goodliff et al, 2019), atmosphere-ocean coupled models (Nerger et al, 2020;Zheng et al, 2020), and hydrological models (Y. Li et al, 2023). In the context of the LESTKF, the EnKF procedure is efficiently formulated, facilitating discussion on the unique aspects of DA with respect to the ensemble filter.…”

“…These experiments allow us to evaluate the performance and effectiveness of WRF-PDAF in assimilating observations and improving the model representation of atmospheric variables. In these twin experiments, synthetic observations are generated directly at the model grid points so that no interpola- Gaussian noise, with a standard deviation of 1.2 K following L. Li et al (2023), is added to the T field of the True run to generate the observations. Each profile represents a single vertical column of observations located at grid points.…”

The Impact of Profiles Data Assimilation on an Ideal Tropical Cyclone Case

“…In the LESTKF method, an adaptive scheme [28] is adopted for the forgetting factor. Observation errors are assigned for temperature (T), horizontal wind (U), and vertical wind (V) at values of 1.2 K, 1.4 m/s, and 1.4 m/s, respectively, following Li et al [11].…”

“…A comparison of the relative impacts of ocean-surface wind measurements and three-dimensional profiles on hurricane forecasts highlights the advantages of 3D wind measurements [10]. The different effects of assimilating only temperature, only winds, and both data types of temperature and wind observations in tropical regions concerning the background state in a perfect model have been explored [11]. However, due to cost limitations and practical constraints, the number of profile instruments cannot be infinite.…”

Added Value of Three‐Dimensional Horizontal Winds From Geostationary Interferometric Infrared Sounder for Typhoon Forecast in a Regional NWP Model

“…Satellitederived wind can monitor the movement of clouds and produce atmospheric motion vectors (AMVs) focusing on cloudy areas but lack the wind profile information (Chen et al, 2020;Li et al, 2020;Lim et al, 2019). Moreover, the Aeolus satellite project has successfully obtained comprehensive wind profiles globally for the first time using the space-based Doppler wind lidar (Li et al, 2023;Stoffelen et al, 2021;Witschas et al, 2020). The assimilation of these wind profiles based on Aeolus significantly enhances the analysis and prediction capabilities of operational NWP models (Garrett et al, 2022;Laroche & St-James, 2022;Pourret et al, 2022;Rennie et al, 2021).…”