In this article, we introduce a fully connected deep neural network algorithm to emulate the Community Cadiative Transfer Model (FCDN_CRTM) simulation of brightness temperatures (BTs) from the Advanced Technology Microwave Sounder (ATMS) channels for clear-sky cases over ocean surfaces. The FCDN_CRTM fine-tuned through three sensitivity experiments with respect to sample-size determination, model separation, and introduction of novel features toward improving the accuracy of the model. In addition to the BT simulation, we evaluated the Jacobians with respect to surface and atmospheric parameters. Atmosphere profiles from the European Centre for Medium-Range Weather Forecasts, sea surface temperature from the Canadian Meteorology Centre, and ATMS sensor data records were used as FCDN_CRTM inputs. In comparison to CRTM, the FCDN_CRTM minus CRTM mean biases were within several hundredths of a Kelvin (K), and the corresponding standard deviations (SDs) were between 0.05 and 0.15 K for all ATMS bands. The accuracies for both mean bias and SD were consistent throughout the evaluation period, which spanned approximately 1 year beyond the period of the FCDN_CRTM training dataset. Furthermore, the model Jacobians generally compared well with CRTM Jacobians in terms of surface temperature, wind speed, air temperature, and (log) water vapor. The performance of the FCDN_CRTM forward and Jacobian model indicate potential for use in data assimilation and physical retrieval systems, such as the NOAA operational Microwave Integrated Retrieval System.

The need for highly accurate atmospheric wind observations is a high priority in the science community, particularly for numerical weather prediction (NWP). To address this need, this study leverages Aeolus wind lidar level-2B data provided by the European Space Agency (ESA) as a potential comparison standard to better characterize atmospheric motion vector (AMV) bias and uncertainty. AMV products from geostationary (GEO) and low Earth orbiting (LEO) satellites are compared with reprocessed Aeolus horizontal line-of-sight (HLOS) global winds observed in August–September 2019. Winds from two Aeolus observing modes are compared with AMVs, namely Rayleigh-clear (RAY; derived from the molecular scattering signal) and Mie-cloudy (MIE; derived from the particle scattering signal). Quality-controlled (QC'd) Aeolus winds are co-located with QC'd AMVs in space and time, and the AMVs are projected onto the Aeolus HLOS direction. Mean co-location differences (MCDs) and the standard deviation (SD) of those differences (SDCDs) are determined and analyzed. As shown in other comparison studies, the level of agreement between AMV and Aeolus wind velocities (HLOSVs) varies with the AMV type, geographic region, and height of the co-located winds, as well as with the Aeolus observing mode. In terms of global statistics, QC'd AMVs and QC'd Aeolus HLOSVs are highly correlated for both observing modes. Aeolus MIE winds are shown to have great potential value as a comparison standard to characterize AMVs, as MIE co-locations generally exhibit smaller biases and uncertainties compared to RAY co-locations. Aeolus RAY winds contribute a substantial fraction of the total SDCDs in the presence of clouds where co-location/representativeness errors are also large. Stratified comparisons with Aeolus HLOSVs are consistent with known AMV bias and uncertainty in the tropics, NH extratropics, the Arctic, and at mid- to upper-levels in clear and cloudy scenes. AMVs in the SH/Antarctic generally exhibit larger-than-expected MCDs and SDCDs, most probably due to larger AMV height assignment errors and co-location/representativeness errors in the presence of high wind speeds and strong vertical wind shear, particularly for RAY comparisons.

Data assimilation is routinely employed in meteorology, engineering and computer sciences to optimally combine noisy observations with prior model information for obtaining better estimates of a state, and thus better forecasts, than achieved by ignoring data uncertainties. Earthquake forecasting, too, suffers from measurement errors and partial model information and may thus gain significantly from data assimilation. We present perhaps the first fully implementable data assimilation method for earthquake forecasts generated by a point-process model of seismicity. We test the method on a synthetic and pedagogical example of a renewal process observed in noise, which is relevant for the seismic gap hypothesis, models of characteristic earthquakes and recurrence statistics of large quakes inferred from paleoseismic data records. To address the non-Gaussian statistics of earthquakes, we use sequential Monte Carlo methods, a set of flexible simulation-based methods for recursively estimating arbitrary posterior distributions. We perform extensive numerical simulations to demonstrate the feasibility and benefits of forecasting earthquakes based on data assimilation.

The European Space Agency Aeolus mission launched a first-of-its-kind spaceborne Doppler wind lidar in August 2018. To optimize the assimilation of the Aeolus Level-2B (B10) horizontal line-of-sight (HLOS) winds, significant systematic differences between the observations and numerical weather prediction (NWP) background winds should be removed. Total least squares (TLS) regression is used to estimate speed-dependent systematic differences between the Aeolus HLOS winds and the National Oceanic and Atmospheric Administration (NOAA) Finite-Volume Cubed-Sphere Global Forecast System (FV3GFS) 6 h forecast winds. Unlike ordinary least squares regression, TLS regression optimally accounts for random errors in both predictors and predictands. Large, well-defined, speed-dependent systematic differences are found in the lower stratosphere and troposphere in the tropics and Southern Hemisphere. Correction of these systematic differences improves the forecast impact of Aeolus data assimilated into the NOAA global NWP system.