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NASA NTRSDate not supplied

Potential Use of Spaceborne Differential Absorption Radar Measurements of Marine Surface Pressure to Improve Weather and Tropical Cyclone Forecasting

Differential absorption radar (DAR) has been identified as a method to remotely sense surface air pressure, particularly over the marine surface. Such an instrument would provide an estimate of the total atmospheric column oxygen content from which the surface air pressure can be calculated. NASA recently funded the development of a demonstrator instrument, the airborne Microwave Barometric Radar and Sounder (MBARS) to test and evaluate this retrieval. The potential impact of surface pressure observations from a spaceborne sensor is evaluated using the updated NASA/GMAO global Observing System Simulation Experiment framework. Initial experiments showing the impact of different orbital configurations of DAR surface pressure measurements on overall NWP will be shown, including Forecast Sensitivity Observation Impact metrics. Case studies of satellite-based surface air pressure retrievals on tropical cyclones will also be discussed.

DAR↗
NASA NTRS2019

Alternative Vegetation States in Tropical Forests and Savannas: The Search for Consistent Signals in Diverse Remote Sensing Data

Globally, the spatial distribution of vegetation is governed primarily by climatological factors (rainfall and temperature, seasonality, and inter-annual variability). The local distribution of vegetation, however, depends on local edaphic conditions (soils and topography) and disturbances (fire, herbivory, and anthropogenic activities). Abrupt spatial or temporal changes in vegetation distribution can occur if there are positive (i.e., amplifying) feedbacks favoring certain vegetation states under otherwise similar climatic and edaphic conditions. Previous studies in the tropical savannas of Africa and other continents using the MODerate Resolution Imaging Spectroradiometer (MODIS) vegetation continuous fields (VCF) satellite data product have focused on discontinuities in the distribution of tree cover at different rainfall levels, with bimodal distributions (e.g., concentrations of high and low tree cover) interpreted as alternative vegetation states. Such observed bimodalities over large spatial extents may not be evidence for alternate states, as they may include regions that have different edaphic conditions and disturbance histories. In this study, we conduct a systematic multi-scale analysis of diverse MODIS data streams to quantify the presence and spatial consistency of alternative vegetation states in Sub-Saharan Africa. The analysis is based on the premise that major discontinuities in vegetation structure should also manifest as consistent spatial patterns in a range of remote sensing data streams, including, for example, albedo and land surface temperature (LST). Our results confirm previous observations of bimodal and multimodal distributions of estimated tree cover in the MODIS VCF. However, strong disagreements in the location of multimodality between VCF and other data streams were observed at 1 km scale. Results suggest that the observed distribution of VCF over vast spatial extents are multimodal, not because of local-scale feedbacks and emergent bifurcations (the definition of alternative states), but likely because of other factors including regional scale differences in woody dynamics associated with edaphic, disturbance, and/or anthropogenic processes. These results suggest the need for more in-depth consideration of bifurcation mechanisms and thus the likely spatial and temporal scales at which alternative states driven by different positive feedback processes should manifest.

Savanna↗
NASA NTRSDate not supplied

Implementing Atmospheric Infrared Sounder (AIRS) and Cross-Track Infrared Sounder (CrIS) Cloud-Clearing Algorithm into the NASA GEOS: Focus on the 2017 Atlantic Tropical Cyclone Season

Numerical Weather Prediction (NWP) centers assimilate cloud-free infrared (IR) radiances because the assimilation of all-sky IR radiances is not yet operationally achievable. The cloud-clearing procedure offers a simpler, but effective strategy that produces cloud-affected radiances suitable for assimilation in partially cloudy regions. Several studies conducted by this team have demonstrated that IR Cloud-Cleared Radiances (CCRs), if thinned more aggressively than clear-sky radiances, can improve analysis and forecasts, particularly in meteorologically active areas. However, CCRs are not used by operational centers due partly to the thought that the process of cloud-clearing may affect latency and introduce difficult-to-control external dependencies. This study presents the results of implementing an Atmospheric Infrared Sounder (AIRS) and Cross-Track Infrared Sounder (CrIS) cloud-clearing procedure into the NASA Goddard Earth Observing System (GEOS) to demonstrate the portability of the procedure. The AIRS and CrIS cloud-clearing algorithms have been deprived of external dependencies, made customizable to any specific model, and the computational efficiency has been improved via parallelization. The revised AIRS and CrIS cloud-clearing algorithms allow a customized choice of channel selection, the use of a user-specified model's fields as first guess, and can perform in real time. Data assimilation experiments with the hybrid 4DEnVar GEOS system were successfully performed for the 2017 tropical cyclones (TC) season with a focus on three major hurricanes (Harvey, Irma, and Maria). This study shows that assimilation of locally-generated CCRs have a positive impact on both global skill and TC representation, compared to the assimilation of AIRS and CrIS clear-sky radiances, and a comparable or slightly improved impact compared to assimilation of CCRs produced by external sources, such as NASA's Distributed Active Archive Centers and NOAA’s Comprehensive Large Array-data Stewardship System. The customization and computational efficiency of the revised procedure would enable its usability in a real-time forecast context.

Niama Boukachaba↗
NASA NTRSDate not supplied

ENSO Tropical Cloud and TOA radiative signatures from the CERES observation

The Clouds and the Earth's Radiant Energy System (CERES) project now has over 15 years accurately observed top-of-the-atmosphere (TOA) flux record for climate monitoring and diagnostic studies. The CERES flux-by-cloud-type dataset, which contains cloud properties and radiative fluxes for 42 cloud types sorted by cloud top pressure and cloud optical depth, is used to investigate the clouds and their associated TOA (top-of-the-atmosphere) fluxes changes over the tropical area during ENSO events during the observed period. Unlike past studies, this study shows the impact of ENSO on cloud properties like optical depth, cloud top effective pressure and temperature and TOA LW and SW fluxes for each sub cloud type. The study reveals the detailed contributions from different cloud types for radiative characteristics during different phases of ENSO. This is especially important for very small net TOA radiative balance due to the cancellation of the fluxes from different cloud types. To further demonstrate the usefulness of this dataset, NCAR Community Atmosphere Model (CAM) is used to simulate the cloud and radiative changes during the period. The model cloud properties are converted to MODIS like cloud properties using modified MODIS simulator. The dataset serves as a more stringent validation of the model for cloud properties and radiative fluxes.

Moguo Sun↗
NASA NTRSDate not supplied

A New ML-Based Adaptive Thinning Methodology to Improve the Impact of AIRS and CrIS Assimilation on Global Tropical Cyclone Forecasts

This work builds on previous research performed by this team to improve the forecast of Tropical Cyclones (TCs) by assimilating AIRS and CrIS radiances into the NASA Global Earth Observing System (GEOS). Past published work demonstrated that the assimilation of radiances with variable density was beneficial to TC forecasting in the GEOS. In the previous setup, a fixed-size moving square named 'TC domain' was activated by the so-called TC-vitals, an international real-time message accessible to all NWP forecasting centers, that documents the existence of a TC, its estimated position, and its size. The information from TC-vitals activated a switch in the GEOS, which allowed to reduce the distance used for thinning AIRS and CrIS data inside a 15 degrees by 15 degrees moving TC domain centered on the storm, so that more data were assimilated in the vicinity of the TC during its lifetime. The methodology produced improved TC analyses and led to better forecasts, particularly related to intensity, without damaging the global forecast skill. In the new version, the adaptive thinning methodology is based on a machine-learning technique. The technique searches for TCs and creates TC masks by using cloud-top temperatures from all geostationary satellites without the need for additional information. It is being trained against the International Best Track Archive for Climate Stewardship (IBTrACS) data base. Once a TC mask is created, a switch identical to the one used in the previous adaptive thinning method is activated, allowing the GEOS to ingest more data in the TC-shaped size-changing domain that follows the storm. As of today, the team has been able to successfully assimilate data inside the ML-detected TC domains. Future work includes an improved capability of reducing false alarm rates (i.e., cloud systems that are erroneously labeled as TCs).

Oreste Reale↗
NASA NTRSDate not supplied

Objectively Identifying Transverse Cirrus Bands in Tropical Cyclones using a Convolutional Neural Network

Transverse cirrus bands (TCBs) are bands of upper-level clouds that are regularly seen in mesoscale and synoptic-scale weather systems. In tropical cyclones, their appearance has been subjectively linked to intensification and the diurnal cycle, but these hypothesized relationships have not been rigorously tested due to the difficulty of objectively identifying TCBs in satellite images. This presentation describes a machine learning technique that successfully identifies TCBs in imagery from the GOES-16 Advanced Baseline Imager. The technique uses a convolutional neural network (CNN) that assigns a probability of each pixel in the image being associated with a TCB. Using the CNN, a database of TCBs from 2018 to 2022 was developed for the Atlantic basin. Statistics using this database will be presented, including the relationship between TCBs and deep-layer vertical wind shear, intensity change, and the TC diurnal cycle.

John Mark Mayhall↗
NASA NTRSDate not supplied

Relating Lightning Flash Characteristics to Convective Structure in Tropical Cyclones

For decades, lightning has been used with some success as an indicator of tropical cyclone (TC) intensity change. An increase in inner-core lightning is usually associated with TC intensification, but this is not always the case. Significant lightning outbreaks have been observed in weakening storms, and hurricanes can rapidly intensify in the total absence of inner-core lightning. These conflicting results arise because lightning production depends on convective and microphysical characteristics that can arise during both intensification and weakening phases in TCs. Recent work using flash size and energy metrics provided by the Geostationary Lightning Mapper (GLM) aboard the GOES-R series of satellites shows some promise in distinguishing between lightning outbreaks that correspond to intensification and those that correspond to weakening. If a hurricane is rapidly intensifying in the presence of inner-core lightning, the size and optical energy of the flashes generally increases as intensification proceeds. It is hypothesized that this relationship is governed by changes in the TC-scale circulation that produce enhanced upper-level outflow and large, strongly helical convective updrafts in the inner core. These changes increase the quantity and spatial extent of charged upper-level ice, which allows large intracloud flashes to occur when electric field breakdown occurs. This presentation will present objective diagnostics of convective asymmetry using azimuthal wavenumber decomposition of GOES-R Advanced Baseline Imager brightness temperatures. The degree of symmetry will be related to GLM flash characteristics to better understand how changes in the symmetry of TC inner-core convection relates to flash rate, size, and energy. It will also show how the evolution of inner-core asymmetries relates to the spatial distribution and characteristics of lightning flashes in the inner core.

Patrick Duran↗
NASA NTRS2024

Tropical Cyclones in the GEOS-S2S-2 Subseasonal Forecasts

This paper analyzes the climatology, prediction skill, and predictability of tropical cyclones (TCs) in NASA’s Global Earth Observing System Subseasonal to Seasonal (GEOS-S2S) forecast system version 2. GEOS reasonably simulates the number and spatial distribution of TCs compared to observations except in the Atlantic where the model simulates too few TCs due to low genesis rates in the Caribbean Sea and Gulf of Mexico. The environmental conditions, diagnosed through a genesis potential index, do not clearly explain model biases in the genesis rates, especially in the Atlantic. At the storm-scale, GEOS reforecasts replicate several key aspects of the thermodynamic and dynamic structure of observed TCs, such as a warm core and the secondary circulation. The model, however, fails to simulate an off-center eyewall when evaluating vertical velocity, precipitation and moisture. The analysis of prediction skill of TC genesis and occurrence shows that GEOS has comparable skill to other global models in WMO S2S archive and that its skill could be further improved by increasing the ensemble size. After calibration, GEOS forecasts are skillful in the Western North Pacific and Southern Indian Ocean up to 20 days in advance. A model-based predictability analysis demonstrates the importance of the Madden-Julian Oscillation (MJO) as a source of predictability of TC occurrence beyond the 14 day lead-time. Forecasts initialized under strong MJO conditions show evidence of predictability beyond week 3. However, due to model biases in the forecast distribution there are notable gaps between MJO-related prediction skill and predictability which require further study.

Jorge L. Garcia-Franco↗
NASA NTRS2024

Highly Dynamic Gamma-Ray Emissions Are Common in Tropical Thunderclouds

Thunderstorms emit fluxes of gamma rays known as gamma-ray glows, sporadically observed by aircraft, balloons and from ground. Glows are observed as increased gamma-ray emissions by tens of percent up to two orders of magnitude above the background, sometimes abruptly terminated by lightning discharges. Glows are produced by the acceleration of energetic electrons in high electric field regions within thunderclouds, and contribute to charge dissipation. Glows are considered as quasi-stationary phenomena, with durations up to a few tens of seconds and spatial scales up to 10-20 kilometers. No measurement of the full extension in space and time of a gamma ray glow region and their occurring frequency has been reported so far. Here we show that tropical thunderclouds over ocean and coastal regions commonly emit gamma rays for hours over areas up to a few thousands of square kilometers. Emission is associated with deep convective cores; it is not uniform and continuous but shows characteristic timescales of 1-10 seconds and even sub-second for individual glows. The dynamics of gamma-glowing thunderclouds starkly contradicts the quasi-stationary picture of glows, but rather resembles that of a huge gamma-glowing «boiling pot» both in pattern and behavior.

M Marisaldi↗
NASA NTRS2024

High Ice Water Content in Tropical Mesoscale Convective Systems (A Conceptual Model)

The phenomenon of high ice water content (HIWC) occurs in mesoscale convective systems (MCSs) when a large number of small ice particles with typical sizes of a few hundred micrometers, concentrations of the order of 10 2 –10 3 L −1 , and IWC exceeding 1 g m −3 are present at high altitudes. HIWC regions in MCSs may extend vertically up to 10 km above the melting layer and horizontally up to hundreds of kilometers, filling large volumes of the convective systems. HIWC has great geophysical significance due to its effect on precipitation formation, the hydrological cycle, and the radiative properties of MCSs. It is also recognized as a hazard for commercial aviation operations since it can result in engine power loss and in the malfunctioning of aircraft data probes. This study summarizes observational and numerical simulation efforts leading to the development of a conceptual model for the production of HIWC in tropical MCSs based on the data collected during the HAIC–HIWC campaign. It is hypothesized that secondary ice production (SIP) in the vicinity of the melting layer plays a key role in the formation and sustainability of HIWC. In situ observations suggest that the major SIP mechanism in the vicinity of the melting layer is related to the fragmentation of freezing drops (FFDs). Both in situ data and numerical simulations suggest that the recirculation of drops through the melting layer led to the amplification of SIP. The proposed conceptual model and simulation results motivate further efforts to extend reproducible laboratory measurements.

high ice water content↗
NASA NTRSDate not supplied

Assimilation of Soil Moisture Observations Over Land Improves Analysis and Prediction of Tropical Cyclone Idai

Soil moisture conditions can impact the circulation and structure of a tropical cyclone (TC) when part or all of the circulation is over land. Dry land surface conditions may lead to faster dissipation of a TC over land, whereas very wet conditions may lead to a prolonged maintenance of its intensity. While this relationship is relatively well understood in theory, applications of these findings in the context of numerical weather prediction (NWP) have been limited. Here we present a case study that explores the potential of improving TC predictions through an improved soil moisture initialization in an NWP framework. Specifically, we examine the impact of assimilating observations from the NASA Soil Moisture Active Passive (SMAP) mission into the NASA Goddard Earth Observing System (GEOS) global weather model on the prediction of South-West Indian Ocean TC Idai (2019). SMAP provides accurate L-band (1.4 GHz) brightness temperatures (Tb) observations that are sensitive to soil moisture globally and at high revisit times of 2-3 days. It has previously been shown that the assimilation of SMAP Tbs significantly improves modeled land surface states. Here we evaluate: (i) forecasts initialized from an analysis that is comparable to the GEOS operational analysis (without SMAP Tb assimilation) and (ii) forecasts initialized from an analysis that additionally assimilates SMAP Tb observations. We find that in the analysis with SMAP assimilation, the TC has a better-defined, more aligned vertical structure over land relative to the control run; moreover, the analyzed TC size, as measured by the wind speed radius, better matches the observed TC size. We further find significant reductions in the forecast intensity error and the forecast along-track error, measured against observations. The largest error reductions occur at lead times of 36 to 72 hours, suggesting that the land with its longer memory gains in importance as a source of predictability at this timescale. An investigation of the underlying mechanisms leading to the skill improvements from SMAP data assimilation revealed that the assimilation of SMAP leads to wetter soil moisture conditions and an increased latent heat flux in the SMAP analysis, which results in a TC with higher column-integrated total moisture content and total energy compared to the control analysis.

Jana Kolassa↗
NASA NTRSDate not supplied

Machine-Learning-Based Adaptive Thinning of CrIS Radiances to Improve Global Tropical Cyclone Analysis and Forecasts

This work is focused on optimizing the assimilation of hyperspectral infrared (IR) radiances from the Cross-track Infrared Sounder (CrIS) with the goal of improving the representation of tropical cyclones (TCs) in global analyses and forecasts. Current operational assimilation systems rely on subsampling IR radiances on a regular thinning grid. A new and improved adaptive methodology based on machine learning (ML) recognizes TCs from geostationary satellite imagery and is implemented in the Goddard Earth Observing System (GEOS) model and data assimilation framework. The ML methodology is extensively trained on existing TC data sets and creates for each TC a dynamic mask, based on the evolving shape and life cycle of that specific event. Once a TC mask is created, a switch is then activated in the data assimilation system to alter the thinning, ingesting more CrIS radiances within the moving mask, thus increasing the TC sampling. After the TC dissipates, the assimilation of CrIS radiances reverts to normal data density. Results of TC segmentation provided by a state-of-the-art generative machine learning model known as the Denoising Diffusion Probabilistic Model (DDPM) are compared to the previously used U-Net model. The new approach surpasses the performance of the previously developed one. The methodology is applied to both clear-sky and cloud-cleared radiances. Benefits from the latter methodology, particularly in improving the structure of TCs and the intensity forecasts, are presented.

Oreste Reale↗
DOE OSTI2025

Duration of O 2 Exposure Determines Dominance of Fe II vs CH 4 Production in Tropical Forest Soils

Temporal fluctuations in redox conditions influence the availability of Fe III and greenhouse gas emissions in humid upland soils. However, the impact of fluctuation duration on biogeochemical processes remains unclear. We hypothesized that rates of Fe III reduction and CH 4 production are sensitive to the duration of soil oxygenation. To test this, surface soil from the Luquillo Forest, Puerto Rico, was subjected to fluctuating redox conditions with an anoxic interval of 6 days followed by oxic intervals of either 8, 24, or 72 h. Shorter oxic intervals enhanced Fe reduction, while longer oxic intervals enhanced CH 4 emissions. As O 2 exposure decreased from 72 to 8 h, Fe reduction rates increased from 0.12 ± 0.02 to 0.26 ± 0.05 mmol kg –1 h –1 , whereas cumulative CH 4 decreased from 44.0 ± 4.7 to 12.7 ± 4.6 μmol kg –1 . 13 C-amino acid spikes were preferentially incorporated into the DNA of iron reducers (Anaeromyxobacter sp.) in the shorter oxic treatment (8 h vs 24 h), suggesting that Fe reducers are less inhibited by shorter periods of oxidation. Conversely, longer oxygen pulses appear to suppress Fe reducers more than methanogens, leading to increased CH 4 emissions. These findings highlight the role of the redox oscillation length in modulating biogeochemical processes and greenhouse gas emissions in soils.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

CAPE Threshold for Lightning Over the Tropical Ocean

Here, we investigate the relationship between convective available potential energy (CAPE), precipitation, the number and size of storms and overshooting tops, and lightning stroke density ($\mathcal{f}$) over the Central America region. While $\mathcal{f}$ increases almost linearly with CAPE 1/2 over land, $\mathcal{f}$ is nearly muted over the ocean when CAPE is small. In the high-CAPE regime, on the contrary, oceanic storms produce as many lightning flashes as land storms. We show that individual oceanic storms are smaller and contain fewer overshooting tops compared to land storms, although the difference exists across low- and high-CAPE regimes. While $\mathcal{f}$ increases as individual storm size increases, the storm size required to produce lightning appears to be disproportionately high in the low-CAPE regime, likely due to the stronger entrainment effect. The entrainment effect on $\mathcal{f}$ in the low-CAPE regime appears to be much weaker over land. Applying the CAPE threshold for lightning over the ocean to the CAPE-based lightning parameterization scheme of Romps et al. (2014), https://doi.org/10.1126/science.1259100 improves its performance, in particular, at representing the land-sea contrast in $\mathcal{f}$.

54 ENVIRONMENTAL SCIENCES↗