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COMPASS-FME Terrestrial Ecosystem Manipulation to Probe the Effects of Storm Treatments (TEMPEST) Experiment Level 1 Sensor Data v2-1

This is the version 2-1 Level 1 (L1) data release for COMPASS-FME environmental sensors located at our Terrestrial Ecosystem Manipulation to Probe the Effects of Storm Treatments (TEMPEST) experimental site. This manipulative, ecosystem-scale TEMPEST experiment addresses the potential for freshwater and estuarine-water disturbance events to alter tree function, species composition, and ecosystem processes in a deciduous coastal forest in MD, USA. The experiment uses a large-unit (2000 m2), un-replicated experimental design, with three 50 m × 40 m plots serving as control, freshwater, and estuarine-water treatments. L1 data are close to raw, but are units-transformed and have out-of-instrument-bounds, out-of-service, and outlier flags added. Duplicates and missing data are removed but otherwise these data are not filtered, and have not been subject to any additional algorithmic or human QA/QC. Any scientific analyses of L1 data should be performed with care. **This dataset will be updated quarterly with new data for the duration of the project** This dataset includes: - An overall dataset README file that describes the current version, gives citation and contact information, etc. - Site- and year-specific folders, each holding variable-specific CSV (comma separated value) data files for each site and plot in that year. - Metadata files within each site-year folder provide full information on data units, expected ranges, contact information, detailed flood times, as well as a general description of the site. - Environmental sensor types that appear in the data files include weather (ClimaVUE50, CS, RM Young, and LI instruments in the graphs below); soil conditions (TEROS12); soil redox state (Redox); groundwater variables (AquaTROLL200 and AquaTROLL600); open water sondes (Exo); tree sap velocity (Sapflow); and system voltage and state (Datalogger). Data are normally logged every 15 minutes. Please see v2-1 TEMPEST L1 Sensor Package Quick Start.pdf for detailed information on data package structure, temporal coverage, and versioning. The TEMPEST flood events occurred on the following dates. They lasted for ~10 hours each day and delivered ~80,000 gallons to each plot; many data streams are available at 1 or 5 minute frequency during these periods. * Tests: Aug 25 (fresh plot) and Sep 9 (salt plot), 2021 * TEMPEST 1: June 22, 2022 * TEMPEST 2: June 6-7, 2023 * TEMPEST 3: June 11-13, 2024 This dataset was updated 2026-03-12: (i) data now go through 2025-12-31 (previous end was 2025-06-30) and (ii) dataset and file names updated to “…v2-1” (previously was “v2-0”).

54 ENVIRONMENTAL SCIENCES↗

COMPASS-FME Terrestrial Ecosystem Manipulation to Probe the Effects of Storm Treatments (TEMPEST) Experiment Level 2 Sensor Data v2-1

This is the version v2-1 Level 2 (L2) data release for COMPASS-FME environmental sensors located at our Terrestrial Ecosystem Manipulation to Probe the Effects of Storm Treatments (TEMPEST) experimental site. This manipulative, ecosystem-scale TEMPEST experiment addresses the potential for freshwater and estuarine-water disturbance events to alter tree function, species composition, and ecosystem processes in a deciduous coastal forest in MD, USA. The experiment uses a large-unit (2000 m2), un-replicated experimental design, with three 50 m × 40 m plots serving as control, freshwater, and estuarine-water treatments. Level 2 (L2) data consist of sensor observations from the COMPASS-FME synoptic sites, TEMPEST, and DELUGE. Compared to the L1 data, these are more consistent (always 15-minute timestamps for the entire year); better QA/QC’d (out of bounds, out of service, and extreme outlier values are removed); and more complete, with a gap-filled time series available alongside the main observations, and additional derived (calculated) variables. L2 data are intended to be rapidly and easily usable in analyses and simulations. However, algorithmic outlier identification always carries the risk of removing valid data, and Level 1 data may be more suitable for analyses that focus on variability or extreme events. This dataset includes: - An overall dataset README file that describes the current version, gives citation and contact information, etc. - Site- and year-specific folders, each holding variable-specific Parquet (a high performance, space efficient format; see https://parquet.apache.org) data files for each site and plot in that year. - Metadata files within each site-year folder provide full information on data units, expected ranges, contact information, detailed flood times, as well as a general description of the site. - Environmental sensor types that appear in the data files include weather (ClimaVUE50, CS, RM Young, and LI instruments in the graphs below); soil conditions (TEROS12); soil redox state (Redox); groundwater variables (AquaTROLL200 and AquaTROLL600); open water sondes (Exo); tree sap velocity (Sapflow); and system voltage and state (Datalogger). Data are reported every 15 minutes. Please see v2-1 TEMPEST L2 Sensor Package Quick Start.pdf for detailed information on data package structure, temporal coverage, and versioning. Data files are in Apache Parquet, a high performance, space efficient format for tabular data. These files can be read using R's `arrow` package (https://arrow.apache.org/docs/r/), with similar tools available in other languages. The TEMPEST flood events occurred on the following dates. They lasted for ~10 hours each day and delivered ~80,000 gallons to each plot; many data streams are available at 1 or 5 minute frequency during these periods. * Tests: Aug 25 (fresh plot) and Sep 9 (salt plot), 2021 * TEMPEST 1: June 22, 2022 * TEMPEST 2: June 6-7, 2023 * TEMPEST 3: June 11-13, 2024

EARTH SCIENCE > ATMOSPHERE > ATMOSPHERIC TEMPERATU↗

Future Response of Coastal Wetlands to Environmental Stresses: Algorithm Comparison of Numerical Models

Coastal wetlands are a critical component of the earth system that strongly influence the global water and biogeochemical cycles. They are also likely important sentinel of climate change. Because eco-geomorphological processes have long been recognized to be important for coastal wetland survival under accelerated sea-level rise (SLR), many eco-geomorphology models have been developed to assess the impact of climate change on coastal wetlands. Although these models differ substantially in complexity and numerical methods, few studies have investigated the algorithm-level uncertainties in these models. In this study, we developed a multiple-algorithm model framework of coastal wetlands that represents coastal hydrodynamics (such as water level, significant wave height and bottom shear stress) and four eco-geomorphological processes: mineral accretion, organic matter accretion, storm surge erosion and landward migration. We validated the model at three representative coastal wetland sites (Venice Lagoon, Plum Island Estuary and Hunter Estuary) for hydrodynamics, mineral accretion and organic matter accretion. Through model-data comparison, we showed that the model can well capture the dynamics of hydrodynamical and eco-geomorphological conditions in the study sites. Importantly, analysis of the multiple-algorithm simulations suggests that differences in the process representation of mineral and organic matter accretion may contribute to the recent contradicting predictions of coastal wetland evolution under accelerated SLR.

54 ENVIRONMENTAL SCIENCES↗

Correlating rainfall with remotely sensed microwave radiation using physically based models

This simulation study evaluates the response of a 21-cm radiometer, measuring the radiation emitted by a bare soil, to varying accumulations of rain. It is shown that correlations between the decrease in emissivity after a rain storm and the total accumulation depend strongly on the physical characteristics of the soil which affect its capacity to hold water. These are primarily soil texture and pre-rain soil moisture. A method is also discussed which would use the numerical models with remotely sensed microwave brightness and surface physical temperatures, along with conventional weather data, to estimate the total accumulation.

Camillo, P. J.↗

In-Situ Environmental Measurements Needed for Future Mars Missions

Existing measurements and modeling studies indicate that the climate and general circulation of the thin, predominately CO2 Martian atmosphere are characterized by large-amplitude variations with a wide range of spatial and temporal scales. Remote sensing observations from Earth-based telescopes and the Mariner 9, Viking, Phobos, and Mars Global Surveyor (MGS) orbiters show that the prevailing climate includes large-scale seasonal variations in surface and atmospheric temperatures (140 to 300 K), dust optical depth (0.15 to 1), and water vapor (10 to 100 precipitable microns). These observations also provided the first evidence for episodic regional and global dust storms that produce even larger perturbations in the atmospheric thermal structure and general circulation. In-situ measurements by the Viking and Mars Pathfinder Landers reinforced these conclusions, documenting changes in the atmospheric pressure on diurnal (5%) and seasonal (>20%) time scales, as well as large diurnal variations in the near-surface temperature (40 to 70 K), wind velocity (0 to 35 m/s), and dust optical depth (0.3 to 6). These in-situ measurements also reveal phenomena with temporal and spatial scales that cannot be resolved from orbit, including rapid changes in near-surface temperatures (+/- 10 K in 10 seconds), large near-surface vertical temperature gradients (+/- 15 K/meter), diurnally-varying slope winds, and dust devils . Modeling studies indicate that these changes are forced primarily by diurnal and seasonal variations in solar insolation, but they also include contributions from atmospheric thermal tides, baroclinic waves (fronts), Kelvin waves, slope winds, and monsoonal flows from the polar caps.

Crisp, D.↗

Solutions Network Formulation Report: Improving NOAA's PORTS(R) Through Enhanced Data Inputs from NASA's Ocean Surface Topography Mission

The Nation uses water-level data for a variety of practical purposes, including nautical charting, maritime navigation, hydrography, coastal engineering, and tsunami and storm surge warnings. Long-term applications include marine boundary determinations, tidal predictions, sea-level trend monitoring, oceanographic research, and climate research. Accurate and timely information concerning sea-level height, tide, and ocean current is needed to understand their impact on coastal management, disaster management, and public health. Satellite altimeter data products are currently used by hundreds of researchers and operational users to monitor ocean circulation and to improve scientists understanding of the role of the oceans in climate and weather. The NOAA (National Oceanic and Atmospheric Administration) National Ocean Service has been monitoring sea-level variations for many years. NOAA s PORTS (Physical Oceanographic Real-Time System) DST (decision support tool), managed by the Center for Operational Oceanographic Products and Services, supports safe and cost-efficient navigation by providing ship masters and pilots with accurate real-time information required to avoid groundings and collisions. This report assesses the capacity of NASA s satellite altimeter data to meet societal decision support needs through incorporation into NOAA s PORTS. NASA has a long heritage of collecting data for ocean research, including its current Terra and Aqua missions. Numerous other missions provide additional important information for coastal management issues, and data collection will continue in the coming decade with such missions as the OSTM (Ocean Surface Topography Mission). OSTM will provide data on sea-surface heights for determining ocean circulation, climate change, and sea-level rise. We suggest that NASA incorporate OSTM altimeter data (C- and Ku-band) into NOAA s PORTS DST in support of NASA s Coastal Management National Application with secondary support to the Disaster Management and Public Health National Applications.

Guest, DeNeice↗

Solutions Network Formulation Report. Improving NOAA's Tides and Currents Through Enhanced Data Inputs from NASA's Ocean Surface Topography Mission

The Nation uses water-level data for a variety of practical purposes, including hydrography, nautical charting, maritime navigation, coastal engineering, and tsunami and storm surge warnings (NOAA, 2002; Digby et al., 1999). Long-term applications include marine boundary determinations, tidal predictions, sea-level trend monitoring, oceanographic research, and climate research. Accurate and timely information concerning sea-level height, tide, and ocean current is needed to understand their impact on coastal management, disaster management, and public health. Satellite altimeter data products are currently used by hundreds of researchers and operational users to monitor ocean circulation and to improve scientists understanding of the role of the oceans in climate and weather. The NOAA (National Oceanic and Atmospheric Administration) National Ocean Service has been monitoring sea-level variations for many years (NOAA, 2006). NOAA s Tides & Currents DST (decision support tool, managed by the Center for Operational Oceanographic Products and Services, is the portal to a vast collection of oceanographic and meteorological data (historical and real-time), predictions, and nowcasts and forecasts. This report assesses the capacity of NASA s satellite altimeter data to meet societal decision support needs through incorporation into NOAA s Tides & Currents.

Guest, DeNeice C.↗

Building Climate Resilience at NASA Ames

NASA Ames Research Center, located at the southern end of the San Francisco Bay (SFB) estuary has identified three primary vulnerabilities to changes in climate. The Ames Climate Adaptation Science Investigator (CASI) workgroup has studied each of these challenges to operations and the potential exposure of infrastructure and employees to an increased frequency of hazards. Sea level rise inundation scenarios for the SFB Area generally refer to projected scenarios in mean sea level rather than changes in extreme tides that could occur during future storm conditions. In the Summer of 2014, high resolution 3-D mapping of the low lying portion of Ames was performed. Those data are integrated with improved sea level inundation scenarios to identify the buildings, basements and drainage systems potentially affected. We will also identify the impacts of sea level and storm surge effects on transportation to and from the Center. This information will help Center Management develop future Master Plans. Climate change will also lead to changes in temperature, storm frequency and intensity. These changes have potential impacts on localized floods and ecosystems, as well as on electricity and water availability. Over the coming decades, these changes are going to be imposed on top of ongoing land use and land cover changes, especially those deriving from continued urbanization and increase in impervious surface areas. These coupled changes have the potential to create a series of cascading impacts on ecosystems, including changes in primary productivity and disturbance of hydrological properties and increased flood risk.The majority of the electricity used at Ames is supplied by hydroelectric dams, which will be influenced by reductions in precipitation or changes in the timing or phase of precipitation which reduces snow pack. Coupled with increased demand for summertime air conditioning and other cooling needs, NASA Ames is at risk for electricity shortfalls. To assess the anticipated energy usage as climate changes, the Ames CASI team is collecting historical energy usage data from Ames facilities, historical weather data, and projected future weather parameters from the CASI Climate subgroup. This data will be incorporated into the RETScreen model to predict how energy usage at Ames will change over the coming century.

climate↗

A New Approach to Monitoring Coastal Marshes for Persistent Flooding

Many areas in coastal Louisiana are below sea level and protected from flooding by a system of natural and man-made levees. Flooding is common when the levees are overtopped by storm surge or rising rivers. Many levees in this region are further stressed by erosion and subsidence. The floodwaters can become constricted by levees and trapped, causing prolonged inundation. Vegetative communities in coastal regions, from fresh swamp forest to saline marsh, can be negatively affected by inundation and changes in salinity. As saltwater persists, it can have a toxic effect upon marsh vegetation causing die off and conversion to open water types, destroying valuable species habitats. The length of time the water persists and the average annual salinity are important variables in modeling habitat switching (cover type change). Marsh type habitat switching affects fish, shellfish, and wildlife inhabitants, and can affect the regional ecosystem and economy. There are numerous restoration and revitalization projects underway in the coastal region, and their effects on the entire ecosystem need to be understood. For these reasons, monitoring persistent saltwater intrusion and inundation is important. For this study, persistent flooding in Louisiana coastal marshes was mapped using MODIS (Moderate Resolution Imaging Spectroradiometer) time series of a Normalized Difference Water Index (NDWI). The time series data were derived for 2000 through 2009, including flooding due to Hurricane Rita in 2005 and Hurricane Ike in 2008. Using the NDWI, duration and extent of flooding can be inferred. The Time Series Product Tool (TSPT), developed at NASA SSC, is a suite of software developed in MATLAB(R) that enables improved-quality time series images to be computed using advanced temporal processing techniques. This software has been used to compute time series for monitoring temporal changes in environmental phenomena, (e.g. NDVI times series from MODIS), and was modified and used to compute the NDWI indices and also the Normalized Difference Soil Index (NDSI). Coastwide Reference Monitoring System (CRMS) water levels from various hydrologic monitoring stations and aerial photography were used to optimize thresholds for MODIS-derived time series of NDWI and to validate resulting flood maps. In most of the profiles produced for post-hurricane assessment, the increase in the NDWI index (from storm surge) is accompanied by a decrease in the vegetation index (NDVI) and then a period of declining water. The NDSI index represents non-green or dead vegetation and increases after the hurricane s destruction of the marsh vegetation. Behavior of these indices over time is indicative of which areas remain flooded, which areas recover to their former levels of vegetative vigor, and which areas are stressed or in transition. Tracking these indices over time shows the recovery rate of vegetation and the relative behavior to inundation persistence. The results from this study demonstrated that identification of persistent marsh flooding, utilizing the tools developed in this study, provided an approximate 70-80 percent accuracy rate when compared to the actual days flooded at the CRMS stations.

Kalcic, M. T.↗

Polarization Lidar Liquid Cloud Detection Algorithm for Winter Mountain Storms

We have collected an extensive polarization lidar dataset from elevated sites in the Tushar Mountains of Utah in support of winter storm cloud seeding research and experiments. Our truck-mounted ruby lidar collected zenith, dual-polarization lidar data through a roof window equipped with a wiper system to prevent snowfall accumulation. Lidar returns were collected at a rate of one shot every 1 to 5 min during declared storm periods over the 1985 and 1987 mid-Jan. to mid-Mar. Field seasons. The mid-barrier remote sensor field site was located at 2.57 km MSL. Of chief interest to weather modification efforts are the heights of supercooled liquid water (SLW) clouds, which must be known to assess their 'seedability' (i.e., temperature and height suitability for artificially increasing snowfall). We are currently re-examining out entire dataset to determine the climatological properties of SLW clouds in winter storms using an autonomous computer algorithm.

Sassen, Kenneth↗

Cloud-Resolved Simulations of Lightning NOx in an Observed Hector Thunderstorm

Cloud chemistry simulations are being performed for a "Hector" storm observed on 16 November 2005 during the SCOUT-03lACTIVE campaigns based in Darwin, Australia. The primary objective of these simulations is to estimate the average production of NO per lightning flash during the storm. The 3-D WRF-AqChem model is being used for these calculations. This modeling package contains the WRF nonhydrostatic cloud-resolving model, online gas- and aqueous-phase chemistry, and a lightning algorithm (Barth et al., 2007). Early morning soundings of temperature, water vapor and winds are used to initialize the model. Surface heating of the Tiwi Islands is simulated in the model to induce convection. Observations from the Egrett, Falcon, Geophysica, and Dornier aircraft in air undisturbed by the storm are used to construct composite initial condition chemical profiles. Convective transport in the model is tested using tracer species such as CO and O3. Lightning flashes observed by the LINET network are input to the model and a lightning placement scheme is used to inject the resulting NO into the simulated cloud. Various scenarios of NO production per flash are used for cloud-to-ground and intracloud flashes in a series of simulations for the storm. Resulting NO, mixing ratios from each simulation are compared with upper tropospheric anvil observations (from the Geophysica and Egrett aircraft) to determine the best fit with the mean NOx at anvil altitudes, the profile shape, and the frequency distribution of NOx values. We will compare the results for lightning NO production from this tropical thunderstorm with similar analyses conducted for several midlatitude and subtropical convective events.

Pickering, Kenneth↗

Martian B Storm Genesis and Evolution: Initial Analysis of Thermal Datasets.

Introduction: Dust lifting on Mars likely occurs primarily as a result of the exchange of momentum between the atmosphere and the surface via saltation. During saltation, sand-sized particles are mobilized but do not enter into suspension. When these larger particles fall back to the surface, kinetic energy is transferred to smaller dust particles which are then lofted into suspension in the atmosphere. Depending on the altitude to which dust is lofted, it can have a significant effect on atmospheric temperatures. As a strong absorber and emitter in the infrared, dust can influence atmospheric heating and modify the global circulation and weather on Mars [1,2]. Although dust is present in Mars’ atmosphere throughout the year, the atmosphere is generally dustier during the second half of the year when Mars is near perihelion. Observations reveal that episodic global-scale dust storms and fairly regular regional-scale dust storms are superimposed on a well-defined and highly repeatable seasonal cycle of dust opacity and associated mid-level atmospheric temperature responses. Kass et al. (2016) used 50 Pa temperature observations from MRO/MCS to identify three highly repeatable time periods during which regional dust storms occur, and designated them the “A”, “B” and “C” storms. While “A” and “C” storms have been studied a fair amount to-date, “B” storms have not yet been investigated in detail. This study explores the generation and evolution of the annually recurring regional dust storm known as the “B” storm, which was identified and categorized by Kass et al. (2016) based on 25 km (50 Pa) temperature observations. The B storm is a southern-hemisphere (SH) phenomenon that originates at the cap edge just after perihelion and which reaches peak intensity during the SH summer solstice, Ls 270. It may originate from the cap edge storms that spawn near the edge of the seasonal CO2 cap during retreat, but the mechanisms for B storm genesis have yet to be determined definitively [1]. Methods: We will use observational data sets and a global climate model (GCM) to investigate “B” regional storms. The data analysis component will include the analysis of imagery from MGS/MOC and MRO/MARCI, and spectroscopic data sets of dust and temperatures from MGS/TES and MRO/MCS with the goal of fully characterizing the behavior of these storms. Both MGS and TES provide data well-suited for temperature analysis at 25 km. MCS measures atmospheric temperature, dust extinction, and water ice extinction at 5 km intervals from the surface to about 80 km. TES measured atmospheric temperatures, column dust and water ice opacities, and column water vapor abundances. Measurements made by TES extended from the surface to about 40 km [1]. At the 50 Pa (25 km) level, local dust events usually confined to shallower depths are effectively filtered out of the analysis leaving the regional dust events identifiable by their temperature signatures [1]. Our preliminary analysis makes use of the fact that the brightness temperature at 15 microns (T15 temperature) is a close approximation to observed temperature at 25 km. We first reproduce the zonal mean 50 Pa level temperature plots for MY 29-32 to establish a baseline for our procedures moving forward [1]. Expanding on Kass et al. (2016), we include recent MCS data from MY 33 and 34 as well. Preliminary Analysis: The daytime (3PM) T15 temperatures in Figure 1 indicate: in MY 29, a strong A storm at Ls 240, a B storm at high southern latitudes just after Ls 270, and a C storm at Ls 320; in MY 30, a B storm at Ls 270; in MY 31 & MY 32, a B storm just before Ls 270; in MY 33, a B storm at Ls 270; and in MY 34, a strong A storm in the northern hemisphere at Ls 210, and a B storm around Ls 270 although there is a data gap. For the B storms, each is indicative of lofted dust and resultant warming. The daytime temperature structure illustrates that the B storm occurs annually around Ls 270 and is confined to high southern latitudes. It reaches its peak intensity around SH summer solstice, Ls 270, consistently for all six MY assessed. Since direct solar heating is absent overnight, the nighttime T15 temperatures (Figure 2) are often useful for differentiating the heat signature of direct solar heating from the dynamical response to that heating. However, in the southern polar latitudes at perihelion the sun does not set and direct solar heating remains present throughout the night. Importantly for our study, dust lofted in the B storm experiences this direct heating day and night for the entirety of its lifetime. The B storm expands as far north as -60 latitude and decays in latitudinal extent more gradually than it grows. This feature is less obvious in the nighttime (3AM) T15 temperatures (Figure 2). The temperature signal is stronger at night for MY 30-33. The warm pool is larger in area relative to the background at night in these four cases. This more uniform warming masks the “tail” feature somewhat, such that it is barely noticeable during these years. Unfortunately, gaps in MCS data in MY 29 and 34 prevent confirmation of the tail feature during those years, however, the B storm temperature signature follows a very different pattern than that described for MY 30-33. MY 29 and 34 appear to show smaller centers of warming at night and larger centers of warming during the day. This is in opposition to that previously described for MY 30-33. Conclusions and Future Work: We will continue investigating the heat signatures of B storms by looking at the total column heating as recorded by TES. We will also look at lower altitudes for patterns that may describe the relationship between B storms and the cap edge storms that develop while the seasonal cap is retreating. In the future, we will use GCM simulations to determine the atmospheric and thermo-dynamic conditions associated with these storms.

Courtney Marylou Batterson↗

(abstract) Deep Space Network Radiometric Remote Sensing Program

Planetary spacecraft are viewed through a troposphere that absorbs and delays radio signals propagating through it. Tropospheric water, in the form of vapor, cloud liquid,and precipitation , emits radio noise which limits satellite telemetry communication link performance. Even at X-band, rain storms have severely affected several satellite experiments including a planetary encounter. The problem will worsen with DSN implementation of Ka-band becausecommunication link budgets will be dominated by tropospheric conditions. Troposphere-induced propagation delays currently limit VLBI accuracy and are significant sources of error for Doppler tracking. Additionally, the success of radio science programs such as satellite gravity wave experiments and atmospheric occultation experiments depends on minimizing the effect of watervapor-induced prop agation delays. In order to overcome limitations imposed by the troposphere, the Deep Space Network has supported a program of radiometric remote sensing. Currently, water vapor radiometers (WVRs) and microwave temperature profilers (MTPs) support many aspects of the Deep Space Network operations and research and development programs. Their capability to sense atmospheric water, microwave sky brightness, and atmospheric temperature is critical to development of Ka-band telemetry systems, communication link models, VLBI, satellite gravity waveexperiments, and r adio science missions. During 1993, WVRs provided data for propagation mode development, supp orted planetary missions, and demonstrated advanced tracking capability. Collection of atmospheric statistics is necessary to model and predict performance of Ka-band telemetry links, antenna arrays, and radio science experiments. Since the spectrum of weather variations has power at very long time scales, atmospheric measurements have been requested for periods ranging from one year to a decade at each DSN site. The resulting database would provide reliable statistics on daily, monthly, and seasonal variations. Only long-term monitoring will prevent biases from being introduced by an exceptionally wet or dry year. Support for planetary missions included tropospheric calibration for the recent Mars Observer gravity wave experiments and Ka-band link experiment (KaBLE). Additionally, several proposed radio science experiments such as profiling planetary atmospheres using satellite occultations and Ka-band gravitational wave searches require advanced radiometer technology development. Finally, there has been a consistent advanced technology program to advance satellite navigational and tracking capabilities. This year that included an experiment with radiometer based tropospheric calibration for a series of VLBI catalog measurements.

microwave propagation Ka-band weather variations r↗

Model data for Flood Frequency Analysis using Stochastic Storm Transposition and an Integrated Surface-Subsurface Hydrological Model

This archived provides scripts and input files used for the implementation of a novel approach to conduct process-based Flood Frequency Analysis using a Stochastic Storm Transposition (SST) and an Integrated Surface-Subsurface Hydrological Model (ISSHM). As a proof-of-concept, this study uses the ISSHM, Advanced Terrestrial Simulator (Amanzi-ATS) model, and the SST model, RainyDay, to conduct flood frequency analysis by simulating the flood response to 5,000 annual synthetic storm events in a ~2000 km2 Southeast Texas watershed.The Watershed Workflow package is implemented in Python3. The Jupyter notebooks can be executed through multiple open-source tools, for example, Anaconda Jupyter Lab, VS Studio Code, etc. Other data files include TXT, CSV, DAT, SBATCH, SHP, TIF, NetCDF, and HDF5 files, which can be read through Python scripts. The input files for the ATS model and RainyDay model have .XML and .SST extensions, respectively, and can be edited in any commonly used text editors.This archive contains:* Scripts and data files essential for generating the ATS model input. It uses the Watershed Workflow package to produce both mesh and ATS input files. * Jupyter notebooks designated for the ATS model evaluation, covering both long-term simulations and 40 rainfall-runoff events.* Input files required to simulate SST storm events using RainyDay.

54 ENVIRONMENTAL SCIENCES↗

Forecasting of Storm-Surge Floods Using ADCIRC and Optimized DEMs

Increasing the accuracy of storm-surge flood forecasts is essential for improving preparedness for hurricanes and other severe storms and, in particular, for optimizing evacuation scenarios. An interactive database, developed by WorldWinds, Inc., contains atlases of storm-surge flood levels for the Louisiana/Mississippi gulf coast region. These atlases were developed to improve forecasting of flooding along the coastline and estuaries and in adjacent inland areas. Storm-surge heights depend on a complex interaction of several factors, including: storm size, central minimum pressure, forward speed of motion, bottom topography near the point of landfall, astronomical tides, and, most importantly, maximum wind speed. The information in the atlases was generated in over 100 computational simulations, partly by use of a parallel-processing version of the ADvanced CIRCulation (ADCIRC) model. ADCIRC is a nonlinear computational model of hydrodynamics, developed by the U.S. Army Corps of Engineers and the US Navy, as a family of two- and three-dimensional finite-element-based codes. It affords a capability for simulating tidal circulation and storm-surge propagation over very large computational domains, while simultaneously providing high-resolution output in areas of complex shoreline and bathymetry. The ADCIRC finite-element grid for this project covered the Gulf of Mexico and contiguous basins, extending into the deep Atlantic Ocean with progressively higher resolution approaching the study area. The advantage of using ADCIRC over other storm-surge models, such as SLOSH, is that input conditions can include all or part of wind stress, tides, wave stress, and river discharge, which serve to make the model output more accurate. To keep the computational load manageable, this work was conducted using only the wind stress, calculated by using historical data from Hurricane Camille, as the input condition for the model. Hurricane storm-surge simulations were performed on an eight-node Linux computer cluster. Each node contained dual 2-GHz processors, 2GB of memory, and a 40GB hard drive. The digital elevation model (DEM) for this region was specified using a combination of Navy data (over water), NOAA data (for the coastline), and optimized Interferometric Synthetic Aperture Radar data (over land). This high-resolution topographical data of the Mississippi coastal region provided the ADCIRC model with improved input with which to calculate improved storm-surge forecasts.

Valenti, Elizabeth↗

Air Pollution Unable to Intensify Storms via Warm‐Phase Invigoration

Abstract According to the hypothesis of aerosol invigoration, the higher concentration of aerosols in polluted air intensifies storms. A leading theory for explaining such a relationship is warm‐phase invigoration, in which cloudy updrafts that are more polluted more readily condense water vapor onto liquid drops, thereby releasing latent heat faster, leading to higher buoyancies and higher updraft speeds. For this mechanism to work, water‐vapor supersaturations well in excess of 1% must be typical of relatively unpolluted cloudy updrafts. Here, the supersaturation is calculated from in situ observations of warm‐phase cloudy updrafts over the Amazon. Instead of values well in excess of 1%, the typical values are found to be around 0.2%. These observations imply that cleaner preindustrial air might have generated supersaturations around 1%, but those are still too low for warm‐phase invigoration to have any practically significant impact on cloud buoyancy and updraft speeds.

54 ENVIRONMENTAL SCIENCES↗

Leveraging Machine Learning and Geo-Tagged Citizen Science Data to Disentangle the Factors of Avian Mortality Events at the Species Level

Abrupt environmental changes can affect the population structures of living species and cause habitat loss and fragmentations in the ecosystem. During August–October 2020, remarkably high mortality events of avian species were reported across the western and central United States, likely resulting from winter storms and wildfires. However, the differences of mortality events among various species responding to the abrupt environmental changes remain poorly understood. In this study, we focused on three species, Wilson’s Warbler, Barn Owl, and Common Murre, with the highest mortality events that had been recorded by citizen scientists. We leveraged the citizen science data and multiple remotely sensed earth observations and employed the ensemble random forest models to disentangle the species responses to winter storm and wildfire. We found that the mortality events of Wilson’s Warbler were primarily impacted by early winter storms, with more deaths identified in areas with a higher average daily snow cover. The Barn Owl’s mortalities were more identified in places with severe wildfire-induced air pollution. Both winter storms and wildfire had relatively mild effects on the mortality of Common Murre, which might be more related to anomalously warm water. Our findings highlight the species-specific responses to environmental changes, which can provide significant insights into the resilience of ecosystems to environmental change and avian conservations. Additionally, the study emphasized the efficiency and effectiveness of monitoring large-scale abrupt environmental changes and conservation using remotely sensed and citizen science data.

47 OTHER INSTRUMENTATION↗

Thermal Emission Spectrometer Results: Mars Atmospheric Thermal Structure and Aerosol Distribution

Infrared spectra returned by the Thermal Emission Spectrometer (TES) are well suited for retrieval of the thermal structure and the distribution of aerosols in the Martian atmosphere. Combined nadir- and limb-viewing spectra allow global monitoring of the atmosphere up to 0.01 mbar (65 km). We report here on the atmospheric thermal structure and the distribution of aerosols as observed thus far during the mapping phase of the Mars Global Surveyor mission. Zonal and temporal mean cross sections are used to examine the seasonal evolution of atmospheric temperatures and zonal winds during a period extending from northern hemisphere mid-summer through vernal equinox (L(sub s) = 104-360 deg). Temperature maps at selected pressure levels provide a characterization of planetary-scale waves. Retrieved atmospheric infrared dust opacity maps show the formation and evolution of regional dust storms during southern hemisphere summer. Response of the atmospheric thermal structure to the changing dust loading is observed. Maps of water-ice clouds as viewed in the thermal infrared are presented along with seasonal trends of infrared water-ice opacity. Uses of these observations for diagnostic studies of the dynamics of the atmosphere are discussed.

Smith, Michael D.↗