Using microwave imager observations to improve the ocean temperature in a coupled NWP system

Impact of Clouds on Infrared and Microwave Sounding Retrieval and an Objective Correction Method for Numerical Weather Prediction

Numerical weather simulations and forecasts are highly sensitive to environmental conditions. This is especially true in Taiwan, an ocean-surrounded island, during its transition season. Atmospheric temperature and moisture profiles retrieved from spaceborne sounders provide essential environmental information in regions lacking in situ observations. However, infrared sounders are sensitive to clouds and may induce uncertainties related to cloud properties. The present study analyzed 1 year of soundings from the National Oceanic and Atmospheric Administration’s Unique Combined Atmospheric Processing System (NUCAPS) to investigate the effects of clouds on the retrievals. The results indicated that the retrieved temperature profiles over land and under clouds had greater uncertainty than over oceans or in clear skies. In addition, the moisture profiles often exhibited a bias against cloud-top pressure. Therefore, this study proposed an objective quality control and bias correction method based on cloud effects. Excluding temperature observations affected by clouds and those over land reduced the root mean square difference from 3.3 K to 1.3 K. The relative cloud-top pressure level was used to conduct water vapor bias correction, which achieved effective correction for dry bias in the retrieved moisture profiles. After appropriate constraint criteria were applied, the bias-corrected profiles demonstrated a reduction in moisture bias from −4% to nearly 0%. That is, we assimilated sounding and radiance data into the regional Weather Research and Forecasting model and evaluated their effects, and we discovered that the retrieved profiles and direct observations positively contributed to the forecast of a spring frontal system. However, experiments using objective-bias-corrected sounding data improved skill scores in precipitation forecasts compared with using original sounding data or radiance data under a standard global operational baseline bias correction.

Improving simulation of Earth system variability through weakly coupled ocean data assimilation in E3SM

Abstract. Accurate initialization of ocean states is essential for skillful prediction of Earth system variability across seasonal-to-decadal timescales. In this study, we evaluate the impact of a newly developed four-dimensional ensemble variational (4DEnVar)-based weakly coupled ocean data assimilation (WCODA) system within the DOE Energy Exascale Earth System Model version 2 (E3SMv2) on global and regional climate variability. By assimilating monthly ocean temperature and salinity from the EN4.2.1 reanalysis into the fully coupled model, we demonstrate substantial improvements in simulating both interannual and decadal climate variability. Compared with the free-running coupled simulation, the assimilation experiment exhibits markedly enhanced interannual correlations with observations for global mean surface air temperature and precipitation anomalies. The temporal variability of key climate modes, including ENSO, the Indian Ocean Dipole, and multidecadal variability in the Pacific and Atlantic Oceans, also shows markedly improved phase agreement with observations. Regional evaluation over the contiguous United States further shows enhanced skill in simulating winter surface air temperature and precipitation, particularly in the northern and southern regions, respectively, with these improvements linked to improved ENSO simulation. Additional hindcast experiments initialized from the WCODA system exhibit no appreciable initialization shock in the early years and reproduce physically coherent ENSO teleconnection patterns, suggesting the dynamical consistency of the coupled initialization framework. These findings underscore the critical role of coupled forecasts in the data assimilation cycle for propagating observational information across Earth system components. By assimilating ocean reanalysis within the fully coupled framework, the WCODA system enables cross-component information exchange among the ocean, atmosphere, and land, thereby generating dynamically consistent initial conditions that support more accurate simulations of Earth system variability and lay the foundation for seasonal-to-decadal prediction applications.

Evaluation and assimilation of Arctic Weather Satellite data in the AROME‐Arctic forecast system

The Arctic Weather Satellite ( AWS ) has been launched on August 16, 2024 as a prototype and proof of concept for the future European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) Polar System (EPS)–Sterna constellation of small satellites. The AWS platform carries an innovative microwave sounder instrument, providing information on temperature, humidity, and surface states. Many studies recently emphasized the critical value of assimilating microwave data in polar regions, demonstrating how improvements also extend to midlatitudes. However, due to the combined uncertainties in the model state and radiative transfer models, the use of microwave observations is still restrained to clear‐sky conditions over open ocean and snow‐free land surfaces in our regional Application de la Recherche à l'Opérationnel à Méso‐échelle (AROME)‐Arctic forecast system. In this article, we present the new capabilities of AWS over the polar regions for July and December 2025. The AWS instrument demonstrated similar performance to other operational microwave instruments currently assimilated in the AROME‐Arctic system. No visible asymmetric patterns in the residual bias of AWS first‐guess departures (FGd) were found, thanks to multiple updates of the local on‐ground processing chain. Observation errors and thinning distances have been tuned using FGd and other existing instruments as reference. The assimilation of AWS demonstrated an overall neutral to positive impact on the 3‐h forecast fields through an improvement (up to 0.6%) of the fit to other satellite instruments (e.g., microwave and infrared data). In terms of forecast scores, the assimilation of AWS in clear‐sky conditions has shown neutral to positive impacts on the forecast skills of the AROME‐Arctic system, with some significant improvements of the humidity at 700 hPa and close to the surface (about 1% for surface and 3%–6% for radiosonde observations). The AROME‐Arctic system is presently being set up towards AWS all‐sky assimilation.

Evaluation of the Infrared Spectral Signature of ARPEGE‐Climat 6.3 in Clear‐Sky Conditions Using IASI Observations

The Earth's top‐of‐atmosphere (TOA) radiative budget is a key feature of the Earth's climate, and broadband TOA observations are widely used to evaluate climate models. However, recent studies have demonstrated that a good match between simulated and observed broadband fluxes can result from error compensation in the spectral dimension. Hence, assessing the spectrally resolved radiative budget is promising. Here, we use 7 years of IASI hyperspectral infrared observations to evaluate ARPEGE‐Climat, the atmospheric component of the CNRM‐CM6 climate model. To this end, synthetic nadir‐looking IASI spectra are computed from the atmospheric profiles and surface properties of an ARPEGE‐Climat amip simulation, using the radiative transfer code RTTOV, and compared to IASI observations for clear‐sky conditions. Over the ocean, ARPEGE‐Climat exhibits a cold brightness temperature bias (∼1 K) in the atmospheric window, attributed to inappropriate sea surface emissivity and sea ice temperature. A cold bias is also found in the water vapor absorption band due to a wet and cold bias in the troposphere. The cold bias is stronger in the C O2 ${\mathrm{O}}_{2}$ band because the cold bias is more pronounced in the stratosphere. Over land, the cold biases are stronger (∼5 K in the atmospheric window), and mostly due to issues in the surface emissivity and skin temperature at high latitudes and above elevated terrain. The mean annual cycle in selected IASI channels is well captured by ARPEGE‐Climat, although slightly underestimated in amplitude. This study demonstrates the potential of hyperspectral infrared observations for objective climate model evaluation.

Multi-Sensor Data Assimilation for Global Soil Moisture Estimation Using the Local Ensemble Transform Kalman Filter

Borey: A High-Resolution Regional Atmosphere-Ocean-Sea Ice-Wave Forecasting System and Hindcast Dataset for the Barents and Kara Seas

Borey is a high-resolution regional modeling and operational forecasting system for the Barents and Kara Seas. It combines WRF for the atmosphere, NEMO-SI3 for the ocean and sea ice, and WW3 for waves on approximately 3--6\,km grids, and generates daily forecasts to 72 hours. We describe the model chain and production workflow and present an accompanying hourly hindcast of surface conditions from August 2015 to August 2023. The archive provides aligned atmosphere, ocean, sea ice, and wave fields for regional marine studies and a baseline for evaluating the operational system. Comparisons with observations and observation-based products show that Borey captures much of the variability in near-surface atmospheric conditions and ocean temperature. Skill in the evaluated WRF, NEMO, and SI3 forecasts changes only modestly across the three-day window. The main limitations are persistent rather than rapidly growing errors: sea surface temperature is generally too cold, sea ice concentration and occurrence are overestimated during seasonal retreat, and significant wave height is underestimated. Borey should therefore complement observation-constrained products. The planned public release will provide hourly surface fields, native grids, provenance information, and validation outputs for regional analysis, model development, and carefully evaluated data-driven forecasting and data-assimilation research.