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NASA NTRS2023

Data Mining for Science of the Sun-Earth Connection as a Single System

Establishing the Sun-Earth connection requires overcoming the challenges of exploring the data from past and current missions and leveraging tools and models (data mining) to create an efficient system treatment of the Sun and heliosphere. However, solar and heliospheric environment data constitute a vast source of information whose potential is far from being optimally exploited. In the next decade, the solar and heliospheric community will have to manage the increasing amount of information coming from new missions, improve reanalysis of data from past and current missions, and create new data products from the application of new methodologies. This complex task is further complicated by practical challenges such as different datasets and catalogs in different formats that may require different pre-processing and analysis tools, and the need for numerous analysis approaches that are not all fully optimized for large volumes of data. While several ongoing efforts aim at addressing these problems, the available datasets and tools are not always used to their full potential often due to lack of awareness of available resources. In this paper, we summarize the issues raised and goals discussed by members of the community during recent conference sessions focused on data mining for science.

Sun-Earth connection↗

NASA NTRSDate not supplied

An Oveview of TROPESS Data Products and Services at the NASA GES DISC

The TRopospheric Ozone and Precursors from Earth System Sounding (TROPESS) project generates Earth System Data Records (ESDRs) of ozone, and other atmospheric constituents (CH4,CO, H2O, HDO, NH3, PAN and temperature) by processing data from multiple satellites through a common retrieval algorithm and ground data system. Satellite Level-1B input data used in generating the TROPESS L2 data products include CrIS NOAA-20 (JPSS-1), CrIS SNPP, AIRS Aqua, OMI Aura, and TROPOMI S5P. The common retrieval framework is known as the MUlti-SpEctra, MUlti-SpEcies, Multi-SEnsors (MUSES) science data processing system (MUSES-SDPS). Several of the TROPESS data products are now available from the NASA Goddard Earth Sciences Data and Information Service Center (GES DISC) for users to download. In this presentation we provide an overview of the various TROPESS data products. These data products can be divided into the following Forward Stream types: Standard Products, Summary Products, and Full-Archival Products. Standard Products are for users that are doing full analysis with avenging kernel and covariance corresponding to retrieved vertical profiles. Summary products have a smaller file size and are more convenient for first-look and rapid analysis, include total and partial columns, as well as column averaging kernels. The Full-Archival Products will contain all information used in creating the data. TROPESS also creates Special Products, provided on an as-needed and as-available basis to support NASA field missions and individual-investigator requests over specific regions. Eventually, TROPESS will also produce and deliver a set of Reanalysis Stream products. Data at the GES DISC are being transitioned into the "Cloud". This will allow users with "Cloud” access to perform data analysis directly on the data without downloading the data to their system. Services, such as subsetting and data visualization, will also be provided for TROPESS data products at the GES DISC.

James Johnson↗

NASA NTRSDate not supplied

4pPA1 - Turbulence Effects on Shaped Booms: Central Composite Design of Modeled Atmospheric Turbulence Parameters for Sonic Boom Propagation

Propagation of sonic booms through turbulence reduces mean sonic boom perception metric levels and also causes considerable variability. NASA’s PCBoom suite of sonic boom acoustic propagation modules includes an approximate method for accounting for the effects of turbulence on traditional N-wave sonic booms. The current implementation is ineffective for shaped sonic booms or low-booms, and it also has limited values for turbulence and ambient input parameters. NASA’s future X-59 low-boom community noise surveys require an accurate estimate of the effects of turbulence in regions across the USA, so the module must be improved. This work presents the methods of selecting which ambient and turbulence parameters should be included in an improved PCBoom turbulence module. Turbulence and ambient data were collected from two atmospheric model databases, the Climate Forecast System Version 2 and European Centre for Medium-Range Weather Forecast Reanalysis Version 5 (ERA5), hourly from 7 AM to 7 PM local time for 10 years at 19 locations across the USA. A fully-factorial propagation analysis using these parameters would be exceedingly computationally expensive. Instead, a central composite design was chosen resulting in 45 combinations of ambient and turbulence parameters. These 45 cases effectively sample the space balancing computational burden.

turbulence↗

NASA NTRS2022

Re-Evaluation of Low Cloud Amount Relationships With Lower-Tropospheric Stability and Estimated Inversion Strength

Lower-tropospheric stability (LTS) and estimated inversion strength (EIS) have a widely accepted relationship with low cloud amount and are key observational foundations for understanding and modeling low-level stratiform clouds. Using the updated surface-based and satellite cloud data, we find that low cloud amount is not as strongly correlated with LTS, and not as sensitive to LTS, as established in the past. EIS does not provide a stronger correlation with low cloud amount than LTS over all eight regions (including the midlatitudes). Further analyzing the relationships between LTS and EIS with different types of low clouds, we find that there is a strong correlation of LTS and EIS with stratocumulus only. This explains the weaker correlation of low cloud fraction (including cumulus, stratocumulus, and stratus) to both LTS and EIS. These results also suggest the need to re-evaluate these relationships in Earth system models. Plain Language Summary: Low-level clouds play an important role in the global radiation budget. Low cloud amount has two established linear relationships with lower-tropospheric stability (LTS) and estimated inversion strength (ETS). These two relationships are re-evaluated here using three separate observational cloud and two reanalysis datasets for three distinct but overlapping time periods. The analysis shows that the strength of the LTS and ETS relationships with low cloud amount varies, depending on the cloud data used. They have a strong relationship with stratocumulus cloud amount only.

Lauren Cutler↗

NASA NTRSDate not supplied

Regional Energy Budget Over Ocean Derived From Satellite Observations

The uncertainty in regional surface energy flux derived by summing all energy flux components is known to be large. In addition, quantifying the regional energy flux uncertainty considering all flux component uncertainties is very difficult. However, this approach is needed to understand regional energy flux components and how these components change with time. The CERES surface radiation budget data product, Edition 4.1 EBAF combined with reanalysis products was used to assess regional surface energy budget over ocean in earlier studies. The CERES team revised the data product and released Edition 4.2 EBAF product in February 2023. Prominent differences from the earlier edition are: 1) no geostationary satellite derived cloud properties are used for surface irradiance computations and 2) MERRA-2 provides temperature and humidity profiles. Combined with surface turbulent fluxes from various products, this study uses the revised EBAF surface irradiances and addresses regional energy budget over ocean. In addition, the uncertainty in regional surface radiation and energy budgets is discussed.

Seiji Kato↗

NASA NTRSDate not supplied

The Transition of Satellite Observations Assimilated in GEOS to JEDI

In order to incorporate the Joint Effort for Data assimilation Integration (JEDI) in the Goddard Earth Observing System (GEOS), which is used for weather, climate, and air quality forecasts and producing reanalysis datasets, it is necessary to validate the observing system in JEDI. NASA’s Global Modeling and Assimilation Office (GMAO), with the Joint Center for Satellite Data Assimilation (JCSDA), is developing the Unified Forward Operator (UFO) and adding all the necessary features to replicate existing capability. Various satellite and conventional observations are assimilated by the Gridpoint Statistical Interpolation (GSI)–based GEOS atmospheric data assimilations system. GMAO has been adding, validating, and updating procedures including the GEOS all-sky microwave radiance assimilation framework to assimilate those observation in UFO. Robust tests are conducted to ensure correct configurations of observational data bias correction (BC), quality control (QC), and observation error in UFO and good agreements between UFO and GSI results. Our work on satellite observations is reported in this presentation.

Jianjun Jin↗

NASA NTRSDate not supplied

CRTM Microwave Cloud Scattering Lookup Tables and Radar Simulator

Microwave observations play a very important role in improving the weather forecasts. Although these observations are routinely assimilated into NWP models in clear-sky conditions, assimilation of all-sky microwave observations is very limited. Two main factors contributing to this limitation are inaccuracy in the input cloud and hydrometeor profiles used as input to the radiative transfer model and also error in scattering calculations performed by the radiative transfer model itself. The Mie theory is used by many fast RT models to estimate the optical properties of single particles. The Mie theory assumes spherical shapes for ice or snow particles with mixture of air and ice. However, hydrometeors scattering radiation at microwave frequencies have different shapes, sizes, and orientations. Therefore, using Mie theory to determine their optical properties leads to large uncertainties in all-sky radiative transfer calculations. The discrete dipole approximation (DDA) which approximates the optical properties of large objects in terms of discrete dipoles has shown promise in calculating the scattering properties of particles with different shapes in the microwave frequencies. This presentation focuses on recent advancements in the CRTM scattering calculations for frozen hydrometeors in the microwave frequencies using the DDA technique. In addition to using stand-alone CRTM calculations using collocated ATMS and reanalysis profiles, the data assimilation experiments conducted using the NOAA FV3GFS forecast system are used to evaluate the scattering improvements. Additionally, the backscattering information from the DDA database was used to implement a radar simulator into CRTM. The radar operator takes advantage of CRTM different modules to calculate clouds absorption and scattering properties. In addition to the forward model both adjoint and tangent linear of the radar simulator are implemented and evaluated as well. The radar simulator is currently being tested within the JEDI/GEOS data assimilation framework to facilitate the assimilation of radar measurements such as CloudSat CPR and GPM DPR into the NASA GEOS model.

Isaac Moradi↗

NASA NTRSDate not supplied

Formation of Tropopause Cirrus clouds by Typhoon-induced gravity waves during the Asian Summer Monsoon: A Case Study from the BATAL 2017 Campaign

Cirrus clouds in the Tropical Tropopause Layer (TTL) have a net warming impact on Earth’s climate and they regulate the amount of water vapor entering the lower stratosphere through dehydration process near the cold-point tropopause. During the Boreal summer, Asian Summer Monsoon (ASM) is an important source of cirrus clouds and humidity in the TTL owing to frequent deep convection. However, better representation of convection and TTL cirrus clouds in global climate models is needed for accurate assessment of their response to changing climate. In this study, we investigate the mechanisms involved in the formation of a tropopause cirrus cloud layer observed during the Balloon measurement campaigns of the Asian Tropopause Aerosol Layer (BATAL) over Hyderabad (17.47 °N, 78.58 °E), India on 23 August 2017. A subvisible cirrus cloud layer (optical thickness ~0.025) was detected by a backscatter sonde (COBALD) onboard a balloon near the cold-point tropopause (CPT, temperature~ -86.4 ° C, altitude~17.9 km) which was later confirmed by the CATS lidar on the ISS. Simultaneous measurements from an optical particle counter (Boulder Counter) revealed the presence of ice crystals smaller than 50 microns in this layer. The formation mechanism responsible for this tropopause cirrus is investigated using a technique combining three-dimensional back-trajectories, satellite observations, and ERA5 reanalysis data. Satellite observations revealed that the overshooting convection associated with a category-3 typhoon Hato, which hit Macau and Hong Kong on 23 August 2017 injected ice into the lower stratosphere. This caused a hydration patch that followed the ASM anticyclone subsequently moving towards Hyderabad. The presence of tropopause cirrus cloud layers in the cold temperature anomalies and updrafts along the back-trajectories indicated towards the role of typhoon-induced gravity waves in their formation. This case study highlights the role of typhoons in influencing the formation of tropopause cirrus clouds through stratospheric hydration and gravity waves in the ASM anticyclone.

Amit Kumar Pandit↗

NASA NTRSDate not supplied

NASA NDE Fracture Critical Detectable Flaw Sizes History and Methodology

NASA requires that NDE methods and inspectors demonstrate 90% Probability of Detection (POD) with 95% confidence for critical flaw sizes when inspecting fracture critical metallic components. NASA addresses the known variability of NDE inspector capability in two ways. The first, Special NDE, requires that every inspector demonstrate the required 90/95 POD, which is resource intensive. The second approach is Standard NDE for which conservative flaw sizes for different NDE methods are provided such that it is expected that most properly trained inspectors will exceed the 90/95 POD requirement. As such, individual POD demonstration testing is not required. The origin of NASA Standard NDE dates to the start of the Space Shuttle Program in the early 1970’s. In the first study to quantitatively assess NDE methods and inspectors, the performance of multiple inspectors was evaluated for different NDE methods using a large set of fatigue cracked specimens. A rudimentary POD analysis was performed to estimate the 90/95 POD flaw size for each inspector for each method. Additionally, the average and standard deviation of the 90/95 POD flaw size across the multiple inspectors was calculated to estimate the flaw size for which 95 percent of inspectors would provide the 90/95 POD capability. These estimated 90/95/95 POD flaw sizes evolved into the NASA Standard NDE flaw sizes still in use for structural analysis five decades later. The methodology for performing Standard NDE POD studies was never documented in NASA requirements. Furthermore, POD analysis methods have significantly evolved since this seminal study. Likewise, NDE methods have improved and there has been a push to reassess Standard NDE flaw sizes for existing methods, and to develop Standard NDE flaw sizes for new methods such as digital radiography. In this study, a Standard NDE POD methodology was developed and baselined using the historical data. This reanalysis of the historical data identified several deficiencies in the original test plan as well as an overall lack of conservatism in the estimated 90/95/95 POD flaw sizes. The results of this historical review and the new methodology are being incorporated into an update of NASA NDE POD requirements.

Nondestructive Evaluation↗

NASA NTRSDate not supplied

Are the Stratospheric Teleconnection Pathways Similar for Fast and Slowly Propagating Madden-Julian Oscillation (MJO) Episodes?

The Madden-Julian Oscillation (MJO) can influence the extratropical circulation on timescales up to several weeks, with a dependence on the MJO characteristics: MJO events that propagate slowly across the Maritime Continent have a stronger impact on Euro-Atlantic weather than fast MJO events. The slow (fast) MJO events are defined as events that take more (less) than 15 (10) days to propagate from the Indian Ocean (phase 3) to the Pacific Ocean (phase 6), and the MJO amplitude has to be greater than 1 for at least three consecutive days in phase 3 and phase 6. Slowly propagating MJO events lead to a stronger North Atlantic Oscillation (NAO) response than fast MJO events, and the positive (negative) NAO response for slow events occur after a lag of 10 days following phase 4 (phases 7-8). Furthermore, the MJO can influence the strength of the stratospheric polar vortex, which in turn can impact the NAO via downward coupling. While the tropospheric pathway for teleconnections from the MJO events with varying phase speeds is well understood, the stratospheric pathways for MJO events with different propagation speeds have yet to be explored. In this talk, I will discuss the stratospheric pathways during fast and slow MJO episodes using reanalysis data with respect to the strength of the Northern Hemisphere stratospheric polar vortex and subsequent downward coupling to the troposphere. This is evident from the zonal wind response within the stratospheric polar vortex at 60N and 10-hPa and the geopotential height response at 500-hPa and 100-hPa.

Priyanka Yadav↗

NASA NTRS2023

Late-Fall Satellite-Based Soil Moisture Observations Show Clear Connections to Subsequent Spring Streamflow

Because runoff production is more efficient over wetter soils, and because soil moisture has an intrinsic memory, soil moisture information can potentially contribute to the accuracy of streamflow predictions at seasonal leads. In this work, we use surface (0-5 cm) soil moisture retrievals obtained with the National Aeronautics and Space Administration’s Soil Moisture Active Passive satellite instrument in conjunction with streamflow measurements taken within 236 intermediate-scale (2,000 – 10,000 km2) unregulated river basins in the conterminous United States to show that late-fall satellite-based surface soil moisture estimates are indeed strongly connected to subsequent springtime streamflow. We thus show that the satellite-based soil moisture retrievals, all by themselves, have the potential to produce skillful seasonal streamflow predictions several months in advance. In poorly instrumented regions, they could perform better than reanalysis soil moisture products in this regard.

Randal D Koster↗

NASA NTRS2020

Sources, frequency, and chemical nature of dust events impacting the United States East Coast

This study examines 14 years (2004–2017) of surface aerosol composition data from the EPA IMPROVE network with a focus on the monthly profile, sources, and chemical nature of extreme dust events (>92nd percentile of fine soil concentration each month) impacting ten sites along the United States East Coast ranging in latitude from Florida to Maine. Based on trajectory, remote sensing, and reanalysis data, dust events were categorized into four source categories: African, Asian, Mix (African + Asian), and Other (anything other than African and Asian). The results reveal that extreme dust events account for between 3.3% and 4.6% of total available days depending on the site. March-April-May (MAM) had the most (174) dust events, followed by June-July-August (JJA) with 172, and then by September-October-November (SON) with 160 and December-January-February (DFJ) with 150. There is a variability in the predominant dust sources based on latitude, with African and Other sources more influential from North Carolina to the south, while Asian and Other were most important from New Jersey to the north. The Mix category is consistently the least frequent dust category at all sites. The African dust category was linked to the highest fine soil levels across the entire East Coast relative to other sources regardless of location. JJA is mostly impacted by African dust for sites ranging from Florida to New Jersey, while MAM is dominated by Asian dust for all sites. Mix events occurred mostly between April and October and Other events were most common outside of MAM and JJA seasons. Seven out of ten sites had Other as the most dominant source. Aerosol constituents organic carbon (OC) and elemental carbon (EC) had higher average concentrations in Other events (2.39 ± 0.78 μg m -3 and 0.79 ± 0.81 μg m -3 , respectively) as compared to the other three source categories, suggestive of regional anthropogenic emissions. Moreover, the ratios of elements (Si:Al, K:Fe, Fe:Ca, Al:Ca) contributing to fine soil and PM 2.5 :PM 10 exhibited distinct values depending on the dust source category and the site. This study builds on the growing evidence of the importance of long-range transport of dust in impacting distant regions and how a variety of sources can impact the U.S. East Coast at all times of the year.

Abdulmonam M. Aldhaif↗

NASA NTRS2023

A Wind-Driven Snow Redistribution Module for Alpine3d V3.3.0: Adaptations Designed for Downscaling Ice Sheet Surface Mass Balance

Ice sheets gain mass via snow accumulation at the ice sheet surface, which is the primary component of surface mass balance. On the Antarctic ice sheet, winds redistribute snow resulting in surface mass balance that is variable in both space and time. Representing wind-driven snow redistribution processes in models is critical for local assessments of surface mass balance, repeat altimetry studies, and interpretation of ice core accumulation records. To this end, we have adapted Alpine3D, an existing distributed snow modeling framework, to downscale Antarctic surface mass balance to horizontal resolutions up to 1 km. In particular, we have introduced a new two-dimensional advection-based wind-driven snow redistribution module that is driven by an offline coupling between WindNinja, a wind downscaling model, and Alpine3D. We then show that large accumulation variability can be at least partially explained by terrain-induced wind speed variations which subsequently redistribute snow around rolling topography. By comparing Alpine3D to airborne-derived snow accumulation measurements within a testing domain over Pine Island Glacier in West Antarctica, we demonstrate that our Alpine3D downscaling approach improves surface mass balance estimates when compared to MERRA-2, a global atmospheric reanalysis which we use as atmospheric forcing. In particular, when compared to MERRA-2, Alpine3D reduces simulated surface mass balance root mean squared error by 23.4 mm w.e.yr−1 (13%) and increases variance explained by 24%. Despite these improvements, Alpine3D still underestimates observed accumulation variability, thus providing an opportunity for future model improvement.

Eric Keenan↗

NASA NTRS2023

An Evaluation of A Physics-Based Firn Model and A Semi-Empirical Firn Model Across the Greenland Ice Sheet (1980–2020)

The Greenland Ice Sheet's (GrIS) firn layer buffers the ice sheet's contribution to sea level rise by storing meltwater in its pore space. However, available pore space and meltwater retention capability is lost due to ablation of the firn layer and refreezing of meltwater as near-surface ice slabs in the firn. Understanding how firn properties respond to climate is important for constraining the GrIS's future contribution to sea level rise in a warming climate. Observations of firn density provide detailed information about firn properties, but they are spatially and temporally limited. Here we use two firn models, the physics-based SNOWPACK model and the Community Firn Model configured with a semi-empirical densification equation (CFM-GSFC), to quantify firn properties across the GrIS from 1980 through 2020. We use an identical forcing (Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2) atmospheric reanalysis) for SNOWPACK and the CFM-GSFC in order to isolate firn model differences. To evaluate the models, we compare simulated firn properties, including firn air content (FAC), to measurements from the Surface Mass Balance and Snow on Sea Ice Working Group (SUMup) dataset of snow and firn density. Both models perform well (mean absolute percentage errors of 14 % in SNOWPACK and 16 % in the CFM-GSFC), though their performance is hindered by the spatial resolution of the atmospheric forcing. In the ice-sheet-wide simulations, the 1980–1995 average spatially integrated FAC (i.e., air volume in the firn) for the upper 100 m is 34 645 km 3 from SNOWPACK and 28 581 km 3 from the CFM-GSFC. The discrepancy in the magnitude of the modeled FAC stems from differences in densification with depth and variations in the sensitivity of the models to atmospheric forcing. In more recent years (2005–2020), both models simulate substantial depletion of pore space. During this period, the spatially integrated FAC across the entire GrIS decreases by 3.2 % (−66.6 km 3 yr −1 ) in SNOWPACK and 1.5 % (−17.4 km 3 yr −1 ) in the CFM-GSFC. These differing magnitudes demonstrate how model differences propagate throughout the FAC record. Over the full modeled record (1980–2020), SNOWPACK simulates a loss of pore space equivalent to 3 mm of sea level rise buffering, while the CFM-GSFC simulates a loss of 1 mm. The greatest depletion in FAC is along the margins and especially along the western margin where observations and models show the formation of near-surface, low-permeability ice slabs that may inhibit meltwater storage.

Megan Thompson-Munson↗

NASA NTRS2023

A Surface Radiation Balance Data Set from Siple Dome in West Antarctica for Atmospheric and Climate Model Evaluation

A field campaign at Siple Dome in West Antarctica during the austral summer 2019-2020 offers an opportunity to evaluate climate model performance, particularly cloud microphysical simulation. Over Antarctic ice sheets and ice shelves, clouds are a major regulator of the surface energy balance, and in the warm season their presence occasionally induces surface melt that can gradually weaken an ice shelf structure. This dataset from Siple Dome, obtained using transportable and solar-poweredequipment, includes surface energy balance measurements, meteorology and cloud remote sensing. To demonstrate how these data can be used to evaluate model performance, comparisons are made with meteorological reanalysis known to give generally good performance over Antarctica (ERA5). Surface albedo measurements show expected variability with observed cloud amount, and can be used to evaluate a model's snowpack parameterization. One case study discussed involves a squall with northerly winds, during which ERA5 fails to produce cloud cover throughout one of the days. A second case study illustrates how shortwave spectroradiometer measurements that encompass the 1.6-micron atmospheric window reveal cloud phase transitions associated with cloud lifecycle. Here, continuously precipitating mixed-phase clouds become mainly liquid water clouds from local morning through the afternoon, not reproduced by ERA5. We challenge researchers to run their various regional or global models in a manner that has the large-scale meteorology follow the conditions of this field campaign, compare cloud and radiation simulations with this Siple Dome dataset, and potentially investigate why cloud microphysical simulations or other model components might produce discrepancies with these observations.

cloud remote sensing↗

NASA NTRSDate not supplied

Near Real Time Air Quality Forecasts Using the NASA GEOS Model

This presentation provides an overview of NASA's Global Modeling and Assimilation Office (GMAO) high-resolution global forecast and reanalysis products for weather, aerosols, and air quality. The NASA Global Earth Observing System (GEOS) model radiatively coupled to GOCART aerosol module assimilates 2-dimensional column-integrated aerosol optical depth (AOD) at one wavelength (550 nm) in order to constrain the model's background AOD in order to have the best historical estimate and forecasts of AOD and particulate matter. Furthermore, the GEOS model has been expanded to provide global near-real-time 5-day forecasts of atmospheric composition at unprecedented horizontal resolution of 0.25 degrees (~25 km). This composition forecast system (GEOS-CF) combines the operational GEOS weather forecasting model with the state-of-the-science GEOS-Chem chemistry module (version 12) to provide detailed analysis of a wide range of air pollutants such as ozone, carbon monoxide, nitrogen oxides, and fine particulate matter (PM2.5).

K Emma Knowland↗

NASA NTRSDate not supplied

Near Real Time Air Quality Forecasts Using the NASA GEOS Model

K Emma Knowland↗

NASA NTRSDate not supplied

Implementation of A New Microwave Scattering Database and A Forward Model for Active Microwave Sensors in CRTM

Radiative transfer models are extensively used for the assimilation of satellite observations into NWP models as well as retrieving geophysical products from satellite measurements. CRTM is a community model developed by NOAA JCSDA and widely used for different purposes requiring RT calculations. CRTM requires bulk scattering lookup tables in order to perform all-sky RT calculations. However, the current CRTM lookup tables for microwave frequencies were generated based on the Mie theory by assuming spherical frozen particles. The scattering lookup tables generated using the DDA technique has shown to largely improve the RT scattering calculations in the MW region. This presentation targets (i) the implementation and validation of a DDA database that was originally developed for the ARTS RT model into CRTM, and (ii) developing the CRTM active sensor module that takes advantage of the backscattering coefficients computed using the DDA method. The DDA database only provides single scattering properties of different habits, while CRTM requires bulk scattering properties. The CRTM cloud coefficients were previously generated based on the effective radius for representing the size of the particles. However, effective radius is neither measurable nor provided by the NWP models, thus need to be estimated from other geophysical variables such as water content. Therefore, in addition to calculating the CRTM bulk scattering properties from the DDA single scattering database, the CRTM was also largely modified to use cloud water content (kg.m-3), instead of effective radius, for performing the interpolation over size/mass of the particles. CRTM already requires water content as input, thus no extra variables are required for performing scattering calculations using the new ARTS DDA database. The CRTM scattering modules search for effective radius in cloud coefficient files and will use the cloud water content if the effective radius dimension is not found in the cloud coefficient files. Figure 1 shows the CRTM simulated brightness temperatures computed using different cloud coefficients versus ATMS observed values over Hurricane Irma on September 7, 2017 at 18:00 UTC. We used all the cloud water content values included in ERA5 with default CRTM/DDA habits for water, rain, snow, ice, hail, and graupel. ERA5 does not provide separate water content values for ice, hail, and graupel, thus the ice water content values were divided between ice, hail, and graupel clouds similar to what was explained in the previous section. In channels with a frequency lower than 90 GHz, emission from water and rain clouds can compensate for cloud scattering so that cloud contaminated Tbs are larger than corresponding clear sky Tbs. The DDA simulations for channels 1-7 largely perform better than the Mie simulations. The DDA simulations show a mix of small negative and positive simulated minus observed values, while the Mie results show large negative biases. The weighting functions for some of the ATMS temperature sounding channels (channels 9-15) peak mostly above the clouds, therefore the measured Tbs become less sensitive to clouds so that the results of both Mie and DDA become very similar. The Mie lookup tables generate excessive scattering for channel 16, but not enough scattering for the water vapor channels. In the specific case of Hurricane Maria, the DDA lookup tables do not generate enough scattering for channel 16, but the DDA results are much more consistent with observations for water vapor channels than for channel 16. It should be noted that the results may vary if we use other habits to represent snow, hail, and graupel in the DDA simulations. Although these results clearly show the advantage of the DDA database over the Mie dataset, different error sources such as error in the observations, displacement of clouds in the ERA5 reanalysis, and also lack of convective clouds or in general errors in the input atmospheric and cloud profiles contribute to the differences between the simulated and observed values. Aside from the improvements in the simulations, a major advantage of the new dataset is a large number of habits that can be used to tune the data assimilation systems to perform well in different weather conditions.

Isaac Moradi↗