Presentations

Keenan Eure, Pennsylvania State University

David Stensrud, Department of Meteorology and Atmospheric Science, Pennsylvania State University 

Yunji Zhang, Department of Meteorology and Atmospheric Science, Pennsylvania State University 

Matthew Kumjian, Department of Meteorology and Atmospheric Science, Pennsylvania State University 

Darrel Kingfield, Global Systems Laboratory, NOAA

Accurate forecasts of the development and evolution of deep, moist convection in convection-allowing models (CAMs) are both a priority and a challenge for the National Oceanic and Atmospheric Administration (NOAA). Additionally, modeling of the microphysical and internal structures of convection is difficult, as this can affect the storm mode, intensity, and longevity. Novel observations from the WSR-88Ds and GOES-16 have the potential to improve the forecasts of deep convection in CAM ensembles. Since the upgrade to the national network of WSR-88Ds was completed in 2013, polarimetric radar data offer a wealth of information about the shape, size, and type of hydrometeors present in clouds. Several distinct polarimetric signatures in early stages of deep convection have been identified, such as the differential reflectivity (ZDR) column. These columns are vertical protrusions of positive ZDR values above the environmental melting level and can aid significantly in characterizing storm updrafts. Information on the updraft location and intensity have potential to improve CAM representation of convection. In addition, GOES-16 infrared all-sky brightness temperatures provide complimentary information on cloud structures and cover that Doppler radars cannot directly measure. To explore the benefits of both types of data, an ensemble data assimilation approach is used with their simultaneous assimilation. The CAM selected for this study is the Advanced Research version of the Weather Research and Forecasting (WRF-ARW) model with the High-Resolution Rapid Refresh (HRRR) configuration. Observations are assimilated using the Ensemble Kalman Filter (EnKF). Different observations in the experiments are conducted jointly and separately, and all experiments include conventional observations. Analysis is conducted using a real case to realize the influence of these observations on different aspects of the convection, and results are presented and discussed.

Kian (Qien) Huang, University of Utah

Zhaoxia Pu, University of Utah

In this study, we developed a coupled land-atmosphere data assimilation capability with the Joint Effort for Data Assimilation Integration (JEDI) system using a limited area version of the NOAA Unified Forecast System (UFS) and Noah-MP land surface model. We examined the cross-covariances between land and atmosphere. The effectiveness of various horizontal and vertical localization schemes was evaluated and tested. The Soil Moisture Active Passive (SMAP) satellite-derived soil moisture observations and near-surface atmospheric data (2-m temperature and humidity) are assimilated into a coupled land-atmosphere data assimilation at different configurations. The impacts of the coupled data assimilation on the short -range weather forecasts, especially the prediction of near-surface atmospheric conditions with the UFS model, are examined.

Kate Huxtable (Met Office)

Tsz Yan Leung (Met Office)

Andrew Lorenc (Met Office)

Neill Bowler (Met Office)

Clémentine Gas (JCSDA)

The Met Office is currently building its next generation DA system as part of the Joint Effort for Data Assimilation Integration (JEDI). For the global EDA, we are exploring two options: an ensemble of independent data assimilations (EIDA) or a more novel approach, Control-Pert. 

The Control-Pert method is designed as a cheaper alternative to an EIDA, and involves running the best available variational DA method on a control member to give a best estimate of an analysis increment, which is added to all ensemble members. For the ensemble members, a similar but cheaper variational approach is used to give an additional increment for each member's perturbation to the control member, which has the function of adjusting the ensemble spread. These ‘pert’ member assimilations run a simpler DA scheme than the control member and introduce linear assumptions..

These methods have both been implemented into the Object-Oriented Prediction System (OOPS) within JEDI, a collection of model-agnostic DA libraries. We are running scientific experiments to compare Control-Pert and EIDA. We note that Control-Pert without additional inflation is not expected to be as accurate as an EIDA, due to the linear assumptions. We are hoping to see how important the approximated nonlinearities are, and if Control-Pert will be a suitable, affordable implementation. 

This presentation will explain the theory behind Control-Pert, some of the challenges in implementing this in JEDI and a look at early results from the comparative experiments.

Bryan M. Karpowicz, UMBC/GESTARII/NASA GMAO

Erica McGrath-Spangler Morgan State University/GESTAR II/NASA GMAO

Hyperspectral Infrared sounders such as IASI, AIRS, and CrIS have long been an integral part of radiance assimilation in numerical weather prediction (NWP), providing vertical profiles of water vapor and temperature information. Principal Component Scores (PCS) are a lossy form of compression that retains most information, such as temperature and moisture, by using a large training set of atmospheric profiles. However, PCS may not well represent profiles which are rare events, such as volcanic eruptions, and drops some sources of random noise. There has been an increased interest in the use of PCS as EUMETSAT plans to distribute future geostationary sounder radiances from MTG-IRS via PCS only. NWP centers use two approaches to deal with PCS: direct assimilation of the PCS by modifying the radiative transfer model to produce PCS and the associated Jacobians, or a simpler approach of decompressing the PCS and reconstructing the radiances back into channel space to allow assimilating radiances without modifications to the data assimilation system. EUMETSAT has developed a PCS product for IASI that has been operational since 2011. We utilize this product opting for the simpler approach, decompressing IASI PCS into channel space, and assimilating those radiances using the GEOS-ADAS. We then compare this with a control using the standard IASI radiance product. Resulting differences in global forecast statistics, differences in Forecast Sensitivity to Observation Impact, along with implications for implementation and quality control are discussed. 

Hyerim Kim, The University of Iowa

Gregory R. Carmichael, The University of Iowa

South Korea is experiencing severe air pollution events, which often exceeds the National Ambient Air Quality Standard. Air pollution in Korea is due to local emissions—from industrialized sources and high population density—and transboundary emissions due to prevailing westerlies from adjacent countries. In order to control air quality, knowing the influences from different locations of sources is crucial. Sensitivity methods from chemical transport models can provide these source contributions to guide us to a better understanding of the sources and pathways. In particular, backward sensitivity method (adjoint) can be computationally efficient when focusing on specific receptors and seeing the sensitivity to their sources and parameters. In this study, we use the Community Multiscale Air Quality (CMAQ) and its adjoint to analyze source contribution focused on bad air quality episodes in South Korea during recent years. We investigate local impacts as well as transboundary impacts from neighboring countries.

Chengzhe Li, Department of Chemical and Biochemical Engineering, University of Iowa

Jun Wang, Department of Chemical and Biochemical Engineering, University of Iowa

Atmospheric aerosols play an important role in Earth's environment, climate change, and public health. Atmospheric chemical transport models (CTMs), such as Unified Inputs (Initial and Boundary conditions) Weather Research and Forecasting model coupled with Chemistry (UI-WRF-Chem), can provide forecast of aerosol distribution and surface air quality, and fill in data gaps where and when satellite data is not available. However, UI-WRF-Chem, like other air quality models, has uncertainties inherently associated with the deficiency in parametrization schemes, emissions, and description of different atmospheric processes. This proposed work seeks to improve UI-WRF-Chem simulation of aerosol mass concentration and aerosol properties (including vertical and size distribution) in United States through the data assimilation (DA) of ground-based observations (AERONET, EPA and PurpleAir PM2.5) and satellite aerosol products (TROPOMI and TEMPO). DA and inverse modeling methods are developed here, from the statistical optimization of emissions based on source-receptor relationship to the Ensemble Transfer Kalman Filter (ETKF) method for optimizing both emission and the atmospheric composition. At locations such as Ethiopia with limited surface observations and lack of satellite-based data of atmospheric composition due to high cloud cover, statistical optimization can be a viable method for using ground-based measurement of PM2.5 concentration to correct the emission inventory, thereby improving the simulation results of PM2.5 concentration by UI-WRF-Chem. ETKF method can be used for DA of concentration and vertical distribution of multiple species of aerosol and trace gases and is proposed here for using TEMPO-based aerosol and trace gas data products. Finally, the machine-learning based approach will be used to improve the model predictions. Preliminary findings indicate that the enhanced UI-WRF-Chem model, incorporating adjusted emission data, exhibits improved agreement with observation diurnal variation curves. Furthermore, preliminary findings demonstrated that the ETKF method can reduce the relative error by 20-50% in model simulations of trace gases.