Data assimilation insights on selecting the most valuable atmospheric measurements

We discuss how objective guidance on selecting the most valuable atmospheric measurements on future Mars spacecraft missions can be provided through already developed Martian atmospheric data assimilation systems, and in particular through Observing System Simulation Experiments (OSSEs) which are widely used to design instruments for the Earth’s atmosphere

Engineering Plasmonic Nanocrystal Coupling Through Template-Assisted Self-Assembly

The construction of materials from nanocrystal building blocks represents a powerful new paradigm for materials design. Just as nature’s materials orchestrate intricate combinations of atoms from the library of the periodic table, nanocrystal “metamaterials” integrate individual nanocrystals into larger architectures with emergent collective properties. The individual nanocrystal “meta-atoms” that make up these materials are themselves each a nanoscale atomic system with tailorable size, shape, and elemental composition, enabling the creation of hierarchical materials with predesigned structure at multiple length scales. However, an improved fundamental understanding of the interactions among individual nanocrystals is needed in order to translate this structural control into enhanced functionality. The ability to form precise arrangements of nanocrystals and measure their collective properties is therefore essential for the continued development of nanocrystal metamaterials. In this dissertation, we utilize template-assisted self-assembly and spatially-resolved spectroscopy to form and characterize individual nanocrystal oligomers. At the intersection of “top-down” and “bottom-up” nanoscale patterning schemes, template-assisted self-assembly combines the design freedom of lithography with the chemical control of colloidal synthesis to achieve unique nanocrystal configurations. Here, we employ shape-selective templates to assemble new plasmonic structures, including heterodimers of Au nanorods and upconversion phosphors, a series of hexagonally-packed Au nanocrystal oligomers, and triangular formations of Au nanorods. Through experimental analysis and numerical simulation, we elucidate the means through which inter-nanocrystal coupling imparts collective optical properties to the plasmonic assemblies. Our self-assembly and measurement strategy offers a versatile platform for exploring optical interactions in a wide range of material systems and application areas

A reanalysis of ozone on Mars from assimilation of SPICAM observations

We have assimilated for the first time SPICAM retrievals of total ozone into a Martian global circulation model to provide a global reanalysis of the ozone cycle. Disagreement in total ozone between model prediction and assimilation is observed between 45°S–10°S from LS=135–180° and at northern polar (60°N–90°N) latitudes during northern fall (LS=150–195°). Large percentage differences in total ozone at northern fall polar latitudes identified through the assimilation process are linked with excessive northward transport of water vapour west of Tharsis and over Arabia Terra. Modelling biases in water vapour can also explain the underestimation of total ozone between 45°S–10°S from LS=135–180°. Heterogeneous uptake of odd hydrogen radicals are unable to explain the outstanding underestimation of northern polar total ozone in late northern fall. Assimilation of total ozone retrievals results in alterations of the modelled spatial distribution of ozone in the southern polar winter high altitude ozone layer. This illustrates the potential use of assimilation methods in constraining total ozone where SPICAM cannot observe, in a region where total ozone is especially important for potential investigations of the polar dynamics

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

Information-based data selection for ensemble data assimilation

Ensemble-based data assimilation is rapidly proving itself as a computationally-efficient and skilful assimilation method for numerical weather prediction, which can provide a viable alternative to more established variational assimilation techniques. However, a fundamental shortcoming of ensemble techniques is that the resulting analysis increments can only span a limited subspace of the state space, whose dimension is less than the ensemble size. This limits the amount of observational information that can effectively constrain the analysis. In this paper, a data selection strategy that aims to assimilate only the observational components that matter most and that can be used with both stochastic and deterministic ensemble filters is presented. This avoids unnecessary computations, reduces round-off errors and minimizes the risk of importing observation bias in the analysis. When an ensemble-based assimilation technique is used to assimilate high-density observations, the data-selection procedure allows the use of larger localization domains that may lead to a more balanced analysis. Results from the use of this data selection technique with a two-dimensional linear and a nonlinear advection model using both in situ and remote sounding observations are discussed

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

The Power of Transient Piezometric Head Data in Inverse Modeling: An Application of the Localized Normal-score EnKF with Covariance Inflation in a Heterogenous Bimodal Hydraulic Conductivity Field

The localized normal-score ensemble Kalman filter (NS-EnKF) coupled with covariance inflation is used to characterize the spatial variability of a channelized bimodal hydraulic conductivity field, for which the only existing prior information about conductivity is its univariate marginal distribution. We demonstrate that we can retrieve the main patterns of the reference field by assimilating a sufficient number of piezometric observations using the NS-EnKF. The possibility of characterizing the conductivity spatial variability using only piezometric head data shows the importance of accounting for these data in inverse modeling.The first author acknowledges the financial support from the China Scholarship Council (CSC). Financial support to carry out this work was also received from the Spanish Ministry of Science and Innovation through project CGL2011-23295.Xu, T.; Gómez-Hernández, JJ.; Zhou, H.; Li, L. (2013). The Power of Transient Piezometric Head Data in Inverse Modeling: An Application of the Localized Normal-score EnKF with Covariance Inflation in a Heterogenous Bimodal Hydraulic Conductivity Field. Advances in Water Resources. 54:100-118. https://doi.org/10.1016/j.advwatres.2013.01.006S1001185

Soil moisture and soil properties estimation in the Community Land Model with synthetic brightness temperature observations

The Community Land Model (CLM) includes a large variety of parameterizations, also for flow in the unsaturated zone and soil properties. Soil properties introduce uncertainties into land surface model predictions. In this paper, soil moisture and soil properties are updated for the coupled CLM and Community Microwave Emission Model (CMEM) by the Local Ensemble Transform Kalman Filter (LETKF) and the state augmentation method. Soil properties are estimated through the update of soil textural properties and soil organic matter density. These variables are used in CLM for predicting the soil moisture retention characteristic and the unsaturated hydraulic conductivity, and the soil texture is used in CMEM to calculate the soil dielectric constant. The following scenarios were evaluated for the joint state and parameter estimation with help of synthetic L-band brightness temperature data assimilation: (i) the impact of joint state and parameter estimation; (ii) updating of soil properties in CLM alone, CMEM alone or both CLM and CMEM; (iii) updating of soil properties without soil moisture update; (iv) the observation localization of LETKF. The results show that the characterization of soil properties through the update of textural properties and soil organic matter density can strongly improve with assimilation of brightness temperature data. The optimized soil properties also improve the characterization of soil moisture, soil temperature, actual evapotranspiration, sensible heat flux, and soil heat flux. The best results are obtained if the soil properties are updated only. The coupled CLM and CMEM model is helpful for the parameter estimation. If soil properties are biased, assimilation of soil moisture data with only state updates increases the root mean square error for evapotranspiration, sensible heat flux, and soil heat flux