Mars Weather and Predictability: Modeling and Ensemble Data Assimilation of Spacecraft Observations

Combining the perspectives of spacecraft observations and the GFDL Mars General Circulation Model (MGCM) in the framework of ensemble data assimilation leads to an improved understanding of the weather and climate of Mars and its atmospheric predictability. The bred vector (BV) technique elucidates regions and seasons of instability in the MGCM, and a kinetic energy budget reveals their physical origins. Instabilities prominent in the late autumn through early spring seasons of each hemisphere along the polar temperature front result from baroclinic conversions from BV potential to BV kinetic energy, whereas barotropic conversions dominate along the westerly jets aloft. Low level tropics and the northern hemisphere summer are relatively stable. The bred vectors are linked to forecast ensemble spread in data assimilation and help explain the growth of forecast errors. Thermal Emission Spectrometer (TES) temperature profiles are assimilated into the MGCM using the Local Ensemble Transform Kalman Filter (LETKF) for a 30-sol evaluation period during the northern hemisphere autumn. Short term (0.25 sol) forecasts compared to independent observations show reduced error (3-4 K global RMSE) and bias compared to a free running model. Several enhanced techniques result in further performance gains. Spatially-varying adaptive inflation and varying the dust distribution among ensemble members improve estimates of analysis uncertainty through the ensemble spread, and empirical bias correction using time mean analysis increments help account for model biases. With bias correction, we estimate a predictability horizon of about 5 sols during which temperature, wind, and surface pressure forecasts initialized from an assimilation analysis are superior to a free running model forecast. LETKF analyses, when compared with the UK reanalysis, show a superior correspondence to independent radio science temperature profiles. Traveling waves in both hemispheres share a correspondence in phase, and temperature differences between the analyses are generally less than 5 K. Assimilation of Mars Climate Sounder (MCS) temperature profiles reveals the importance of vertical distributions of dust and water ice aerosol in reducing model bias. A strategy for assimilation of TES and MCS aerosol products is outlined for future work

Physically Explainable Deep Learning for Convective Initiation Nowcasting Using GOES-16 Satellite Observations

Convection initiation (CI) nowcasting remains a challenging problem for both numerical weather prediction models and existing nowcasting algorithms. In this study, object-based probabilistic deep learning models are developed to predict CI based on multichannel infrared GOES-R satellite observations. The data come from patches surrounding potential CI events identified in Multi-Radar Multi-Sensor Doppler weather radar products over the Great Plains region from June and July 2020 and June 2021. An objective radar-based approach is used to identify these events. The deep learning models significantly outperform the classical logistic model at lead times up to 1 hour, especially on the false alarm ratio. Through case studies, the deep learning model exhibits the dependence on the characteristics of clouds and moisture at multiple levels. Model explanation further reveals the model's decision-making process with different baselines. The explanation results highlight the importance of moisture and cloud features at different levels depending on the choice of baseline. Our study demonstrates the advantage of using different baselines in further understanding model behavior and gaining scientific insights

Toward More Realistic Simulation and Prediction of Dust Storms on Mars

Global dust storms have major implications for the past and present climate, geologic history, habitability, and future exploration of Mars. Yet their mysterious origins mean we remain unable to realistically simulate or predict them. We identify four key Knowledge Gaps and make four Recommendations to make progress in the next decade

Spatial and Temporal Variation in PBL Height over the Korean Peninsula in the KMA Operational Regional Model

Spatial and temporal variations in planetary boundary layer height (PBLH) over the Korean Peninsula and its surrounding oceans are investigated using a regional grid model operated at the Korea Meteorological Administration (KMA). Special attention is placed on daily maximum mixing height for evaluation against two radiosonde observation datasets. In order to construct a new high-resolution PBLH database with 3-hour time and 10 km spatial resolution, short-term integrations with the regional model are carried out for a one-year period from June 2010 to May 2011. The resulting dataset is then utilized to explore the seasonal patterns of horizontal PBLH distribution over the peninsula for one year. Frequency distributions as well as monthly and diurnal variations of PBLH at two selected locations are examined. This study reveals specific spatiotemporal structure of boundary layer depth over the Korean Peninsula for the first time at a relatively high-resolution scale. The results are expected to provide insights into the direction for operational tuning and future development in the model boundary layer schemes at KMA

Impact of assimilation window length on diurnal features in a Mars atmospheric analysis

Effective simulation of diurnal variability is an important aspect of many geophysical data assimilation systems. For the Martian atmosphere, thermal tides are particularly prominent and contribute much to the Martian atmospheric circulation, dynamics and dust transport. To study the Mars diurnal variability and Mars thermal tides, the Geophysical Fluid Dynamics Laboratory Mars Global Climate Model with the 4D-local ensemble transform Kalman filter (4D-LETKF) is used to perform an analysis assimilating spacecraft temperature retrievals. We find that the use of a ‘traditional’ 6-hr assimilation cycle induces spurious forcing of a resonantly enhanced semi-diurnal Kelvin waves represented in both surface pressure and mid-level temperature by forming a wave 4 pattern in the diurnal averaged analysis increment that acts as a ‘topographic’ stationary forcing. Different assimilation window lengths in the 4D-LETKF are introduced to remove the artificially induced resonance. It is found that short assimilation window lengths not only remove the spurious resonance, but also push the migrating semi-diurnal temperature variation at 50 Pa closer to the estimated ‘true’ tides even in the absence of a radiatively active water ice cloud parameterisation. In order to compare the performance of different assimilation window lengths, short-term to mid-range forecasts based on the hour 00 and 12 assimilation are evaluated and compared. Results show that during Northern Hemisphere summer, it is not the assimilation window length, but the radiatively active water ice clouds that influence the model prediction. A ‘diurnal bias correction’ that includes bias correction fields dependent on the local time is shown to effectively reduce the forecast root mean square differences between forecasts and observations, compensate for the absence of water ice cloud parameterisation and enhance Martian atmosphere prediction. The implications of these results for data assimilation in the Earth's atmosphere are discussed

Verification of operational numerical weather prediction model forecasts of precipitation using satellite rainfall estimates over Africa

Abstract Rainfall is an important variable to be able to monitor and forecast across Africa, due to its impact on agriculture, food security, climate‐related diseases and public health. Numerical weather models (NWMs) are an important component of this work, due to their complete spatial coverage, high resolution and ability to forecast into the future. In this study, the spatio‐temporal skill of short‐term forecasts of rainfall across Africa from 2016 through 2018 is evaluated. Specifically, the European Centre for Medium‐Range Weather Forecasts (ECMWF) and the National Centers for Environmental Prediction‐Global Forecast System (NCEP‐GFS) forecast models are verified by Rainfall Estimates 2.0 (RFE2) and African Rainfall Climatology Version 2 (ARC2), which are fused products of satellite and in situ observations and are commonly used in analysis of African rainfall. Model rainfall forecasts show good consistency with the satellite rainfall observations in spatial distribution over Africa on the seasonal timescale. Evaluation metrics of daily and weekly forecasts show high spatial and seasonal variations over the African continent, including a strong link to the location of the inter‐tropical convergence zone (ITCZ) and topographically enhanced precipitation. The rainfall forecasts at 1 week aggregation time are improved against daily forecasts