Satellite All-sky Infrared Radiance Assimilation: Recent Progress and Future Perspectives

Satellite infrared (IR) sounder and imager measurements have become one of the main sources of data used by data assimilation systems to generate initial conditions for numerical weather prediction (NWP) models and atmospheric analysis/reanalysis. This paper reviews the development of satellite IR data assimilation in NWP in recent years, especially the assimilation of all-sky satellite IR observations. The major challenges and future directions are outlined and discussed.

Potential Impacts of Assimilating Every-10-Minute Himawari-8 Satellite Radiance with the POD-4DEnVar Method

The Advanced Himawari Imager (AHI) onboard the Himawari-8 geostationary satellite provides continuous observations every 10 min. This study investigates the assimilation of every-10-min radiance from the AHI with the POD-4DEnVar method. Cloud detection is conducted in the AHI quality control procedure to remove cloudy and precipitation-affected observations. Historical samples and physical ensembles are combined to construct four-dimensional ensembles according to the observed frequency of the Himawari-8 satellite. The purpose of this study was to test the potential impacts of assimilating high temporal resolution observations with POD-4DEnVar in a numerical weather prediction (NWP) system. Two parallel experiments were performed with and without Himawari-8 radiance assimilation during the entire month of July 2020. The results of the experiment with radiance assimilation show that it improves the analysis and forecast accuracy of geopotential, horizontal wind field and relative humidity compared to the experiment without radiance assimilation. Moreover, the equitable threat score (ETS) of 24-h accumulated precipitation shows that assimilating Himawari-8 radiance improves the rainfall forecast accuracy. Improvements were found in the structure, amplitude and location of the precipitation. In addition, the ETS of hourly accumulated precipitation indicates that assimilating high temporal resolution Himawari-8 radiance can improve the prediction of rapidly developed rainfall. Overall, assimilating every-10-min AHI radiance from Himawari-8 with POD-4DEnVar has positive impacts on NWP.

Experimental OMPS Radiance Assimilation through One-Dimensional Variational Analysis for Total Column Ozone in the Atmosphere

This experiment is the first ultraviolet radiance assimilation for atmospheric ozone in the troposphere and stratosphere. The experiment has provided better understanding of which observations need to be assimilated, what bias correction scheme may be optimal, and how to obtain surface reflectance. A key element is the extension of the Community Radiative Transfer Model (CRTM) to handle fully polarized radiances, which presents challenges in terms of computational resource requirements. In this study, a scalar (unpolarized) treatment of radiances was used. The surface reflectance plays an important role in assimilating the nadir mapper (NM) radiance of the Ozone Mapping and Profiler Suite (OMPS). Most OMPS NM measurements are affected by the surface reflection of solar radiation. We propose a linear spectral reflectance model that can be determined inline by fitting two OMPS NM channel radiances at 347.6 and 371.8 nm because the two channels have near zero sensitivity on atmospheric ozone. Assimilating a transformed reflectance measurement variable, the N value can overcome the difficulty in handling the large dynamic range of radiance and normalized radiance across the spectrum of the OMPS NM. It was found that the error in bias correction, surface reflectance, and neglecting polarization in radiative transfer calculations can be largely mitigated by using the two estimated surface reflectance. This study serves as a preliminary demonstration of direct ultraviolet radiance assimilation for total column ozone in the atmosphere.

Outcomes of the Aerosol Radiance Assimilation Study

<p>The ESA-funded Aerosol Radiance Assimilation Study (ARAS) has provided ground-breaking research in using visible radiance data from satellite to estimate the concentration of aerosols.</p><p>Satellite observations in the infrared and microwave parts of the spectrum have long been assimilated into forecasting systems to help estimate the best possible initial conditions for global weather predictions. Assimilating radiances in the visible part of the spectrum, on the other hand, continues to pose many challenges.The reason lies in the complex interactions of cloud and aerosol particles with radiation at those wavelengths and in the complex characteristics of the surface as a reflector of visible light. These factors make it difficult to develop an observation operator, which converts model values into satellite observation equivalents.</p><p>One of the key achievements of ARAS is to have developed an observation operator for aerosol reflectances in the visible part of the spectrum. This operator was comprised of two elements: a fast radiative transfer code based on a Look-Up-Table approach developed by RAL Space for aerosol retrievals (Thomas et al, 2009) and adapted to the ECMWF’s Integrated Forecast System as well as a surface reflectance model for ocean and land.</p><p>This enabled the first-ever experimental assimilation of reflectances into the 4D-Var assimilation system of ECMWF’s Integrated Forecasting System (IFS) to help estimate aerosol concentrations. The assimilation experiments were very successful. The performance was remarkable considering that this was a new development rolled out over the course of just two years. The observations used in the ARAS project were cloud-cleated aerosol reflectances from the MODIS instrument on board the Aqua and Terra satellites. Experiments were carried out to compare the impact of assimilating these observations with the impact of assimilating traditional satellite-derived AOD observations. The results show that the performance of reflectance assimilation is broadly comparable to that of satellite AOD assimilation. However, it varies depending on the metrics used and the period analysed.</p><p>While assimilating aerosol reflectances is still experimental, the results show great potential for future operational implementation in atmospheric composition forecasts. Such forecasts are routinely produced by the EU‐funded Copernicus Atmosphere Monitoring Service (CAMS) implemented by ECMWF. However, the scope for future applications is much wider than that. Many of the tools developed in ARAS for aerosol visible reflectance assimilation could be adapted to clouds. This could open the way towards a fuller exploitation of visible radiances to improve numerical weather prediction.</p><p><strong>References</strong></p><p>Thomas G.E., Carboni E., Sayer A.M., Poulsen C.A., Siddans R., Grainger R.G. (2009) Oxford-RAL Aerosol and Cloud (ORAC): aerosol retrievals from satellite radiometers. In: Kokhanovsky A.A., de Leeuw G. (eds) Satellite Aerosol Remote Sensing over Land. Springer Praxis Books. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69397-0_7</p>

Progress toward Cloud-Cleared Infrared radiance assimilation in a global modeling framework: Application to the 2017 Atlantic Tropical Cyclone Season.

<p>Previous work by this team has demonstrated that assimilation of IR radiances in partially cloudy regions is beneficial to numerical weather predictions (NWPs), improving the representation of tropical cyclones (TCs) in global analyses and forecasts. The specific technique used by this team is based on the “cloud-clearing CC” methodology. Cloud-cleared hyperspectral IR radiances (CCRs), if thinned more aggressively than clear-sky radiances, have shown a strong impact on the analyzed representation and structure of TCs. However, the use of CCRs in an operational context is limited by 1) latency; and 2) external dependencies present in the original cloud-clearing algorithm. In this study, the Atmospheric InfraRed Sounder (AIRS) CC algorithm was (a) ported to NASA high end computing resources (HEC), (b) deprived of external dependencies, and (c) parallelized improving the processing by a factor of 70. The revised AIRS CC algorithm is now customizable, allowing user’s choice of channel selection, user’s model's fields as first guess, and could perform in real time. This study examines the benefits achieved when assimilating CCRs using the NASA’s Goddard Earth Observing System (GEOS) hybrid 4DEnVar system. The focus is on the 2017 Atlantic hurricane season with three infamous hurricanes (Harvey, Irma, and Maria) investigated in depth.  The impact of assimilating customized CCRs on the analyzed representation of tropical cyclone horizontal and vertical structure and on forecast skill is discussed.</p>

Development of an Ozone Monitoring Instrument (OMI) aerosol index (AI) data assimilation scheme for aerosol modeling over bright surfaces – a step toward direct radiance assimilation in the UV spectrum

Using the Vector LInearized Discrete Ordinate Radiative Transfer (VLIDORT) code as the main driver for forward model simulations, a first-of-its-kind data assimilation scheme has been developed for assimilating Ozone Monitoring Instrument (OMI) aerosol index (AI) measurements into the Naval Aerosol Analysis and Predictive System (NAAPS). This study suggests that both root mean square error (RMSE) and absolute errors can be significantly reduced in NAAPS analyses with the use of OMI AI data assimilation when compared to values from NAAPS natural runs. Improvements in model simulations demonstrate the utility of OMI AI data assimilation for aerosol model analysis over cloudy regions and bright surfaces. However, the OMI AI data assimilation alone does not outperform aerosol data assimilation that uses passive-based aerosol optical depth (AOD) products over cloud-free skies and dark surfaces. Further, as AI assimilation requires the deployment of a fully multiple-scatter-aware radiative transfer model in the forward simulations, computational burden is an issue. Nevertheless, the newly developed modeling system contains the necessary ingredients for assimilation of radiances in the ultraviolet (UV) spectrum, and our study shows the potential of direct radiance assimilation at both UV and visible spectrums, possibly coupled with AOD assimilation, for aerosol applications in the future. Additional data streams can be added, including data from the TROPOspheric Monitoring Instrument (TROPOMI), the Ozone Mapping and Profiler Suite (OMPS), and eventually the Plankton, Aerosol, Cloud and ocean Ecosystem (PACE) mission.