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DOE OSTI2025

Classifying thermodynamic cloud phase using machine learning models

Vertically resolved thermodynamic cloud-phase classifications are essential for studies of atmospheric cloud and precipitation processes. The Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Thermodynamic Cloud Phase (THERMOCLDPHASE) value-added product (VAP) uses a multi-sensor approach to classify the thermodynamic cloud phase by combining lidar backscatter and depolarization, radar reflectivity, Doppler velocity, spectral width, microwave-radiometer-derived liquid water path, and radiosonde temperature measurements. The measured pixels are classified as ice, snow, mixed phase, liquid (cloud water), drizzle, rain, and liq_driz (liquid+drizzle). We use this product as the ground truth to train three machine learning (ML) models to predict the thermodynamic cloud phase from multi-sensor remote sensing measurements taken at the ARM North Slope of Alaska (NSA) observatory: a random forest (RF), a multi-layer perceptron (MLP), and a convolutional neural network (CNN) with a U-Net architecture. Evaluations against the outputs of the THERMOCLDPHASE VAP with 1 year of data show that the CNN outperforms the other two models, achieving the highest test accuracy, F1 score, and mean intersection over union (IOU). Analysis of ML confidence scores shows that ice, rain, and snow have higher confidence scores, followed by liquid, while mixed, drizzle, and liq_driz have lower scores. Feature importance analysis reveals that the mean Doppler velocity and vertically resolved temperature are the most influential data streams for ML thermodynamic cloud-phase predictions. Lidar measurements exhibit lower feature importance due to rapid signal attenuation caused by the frequent presence of persistent low-level clouds at the NSA site. The ML models' generalization capacity is further evaluated by applying them at another Arctic ARM site in Norway using data taken during the ARM Cold-Air Outbreaks in the Marine Boundary Layer Experiment (COMBLE) field campaign. The models demonstrated similar performance to that observed at the NSA site. Finally, we evaluate the ML models' response to simulated instrument outages and signal degradation and show that a CNN U-Net model trained with input channel dropouts performs better when input fields are missing.

ARM Aerial Facility↗

DOE OSTI2025

Thermodynamic Cloud Phase Classifications Using Machine Learning at NSA and ANX

Vertically resolved thermodynamic cloud phase classifications are essential for studies of atmospheric cloud and precipitation processes. The Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) THERMOCLDPHASE Value-Added Product (VAP) uses a multi-sensor approach to classify thermodynamic cloud phase by combining lidar backscatter and depolarization, radar reflectivity, Doppler velocity, spectral width, microwave radiometer-derived liquid water path, and radiosonde temperature measurements. The measured voxels are classified as ice, snow, mixed-phase, liquid (cloud water), drizzle, rain, and liq_driz (liquid+drizzle). We use this product as the ground truth to train three machine learning (ML) models to predict the thermodynamic cloud phase from multi-sensor remote sensing measurements taken at the ARM North Slope of Alaska (NSA) observatory: a random forest (RF), a multilayer perceptron (MLP), and a convolutional neural network (CNN) with a U-Net architecture. Evaluations against the outputs of the THERMOCLDPHASE VAP with one year of data show that the CNN outperforms the other two models, achieving the highest test accuracy, F1-score, and mean Intersection over Union (IOU). Analysis of ML confidence scores shows ice, rain, and snow have higher confidence scores, followed by liquid, while mixed, drizzle, and liq_driz have lower scores. Feature importance analysis reveals that the mean Doppler velocity and vertically resolved temperature are the most influential datastreams for ML thermodynamic cloud phase predictions. The ML models’ generalization capacity is further evaluated by applying them at another Arctic ARM site in Norway using data taken during the ARM Cold-Air Outbreaks in the Marine Boundary Layer Experiment (COMBLE) field campaign. Finally, we evaluate the ML models’ response to simulated instrument outages and signal degradation.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS1986

Reduction of weather effects in the calculation of sea ice concentration from microwave radiances

A technique is presented which improves existing methods of calculating sea ice concentrations from microwave radiances by reducing weather-related effects over open ocean areas and in the vicinity of marginal sea ice zones. Winds, atmospheric water vapor, cloud liquid water, and rain increase the microwave emission over these regions and thus result in erroneous values of computed sea ice concentration. The method described is based on the microwave spectral properties of sea ice and ice-free ocean and utilizes ratios of the polarized radiances at the 0.81-cm (37 GHz) and 1.7-cm (18 GHz) wavelengths. Following a discussion of the physical basis for this technique, examples are provided which demonstrate its utility. While the technique was developed for use with the Nimbus 7 scanning multichannel microwave radiometer data, it is applicable also to data from other microwave radiometers operating in a similar wavelength range.

Gloersen, P.↗

NASA NTRS1990

In-situ measurements for validation of microwave data - Problems, accuracies

This review covers the relationship between remotely sensed microwave data and data obtained in situ or from earth-based remote-sensing systems, both for validation of algorithms and as supplemental information for deriving a geophysical quantity. Parameters obtained from satellite microwave radiometers, the Scanning Multichannel Microwave Radiometer, and the Special Sensor Microwave Imager are emphasized. These include sea-surface temperature and wind speed, integrated atmospheric water vapor, cloud liquid water, and rain rate.

Katsaros, Kristina B.↗

NASA NTRS1992

Investigation of antenna pattern constraints for passive geosynchronous microwave imaging radiometers

Progress by investigators at Georgia Tech in defining the requirements for large space antennas for passive microwave Earth imaging systems is reviewed. In order to determine antenna constraints (e.g., the aperture size, illumination taper, and gain uncertainty limits) necessary for the retrieval of geophysical parameters (e.g., rain rate) with adequate spatial resolution and accuracy, a numerical simulation of the passive microwave observation and retrieval process is being developed. Due to the small spatial scale of precipitation and the nonlinear relationships between precipitation parameters (e.g., rain rate, water density profile) and observed brightness temperatures, the retrieval of precipitation parameters are of primary interest in the simulation studies. Major components of the simulation are described as well as progress and plans for completion. The overall goal of providing quantitative assessments of the accuracy of candidate geosynchronous and low-Earth orbiting imaging systems will continue under a separate grant.

Gasiewski, A. J.↗

NASA NTRS1993

Properties of the Central American cold surge

The Central American cold surge (CACS) is a frontal incursion from the United States into Central America and resembles the East Asian cold surge. They occur more frequently than analyzed by NMC or by published results, based on our observations between 1979 and 1990. Climatology and structure are quantified, based on surface and upper air stations throughout Central America and satellite products from GOES visible and infrared sensors and SSM/I precipitable water and rain rate sensors.

Mcguirk, James P.↗

NASA NTRS1996

Planetary Perspective on Life on Early Mars and the Early Earth

Impacts of asteroids and comets posed a major hazard to the continuous existence of early life on Mars as on the Earth. The chief danger was presented by globally distributed ejecta, which for very large impacts takes the form of transient thick rock vapor atmospheres; both planets suffered such impacts repeatedly. The exposed surface on both planets was sterilized when it was quickly heated to the temperature of condensed rock vapor by radiation and rock rain. Shallow water bodies were quickly evaporated and sterilized. Any surviving life must have been either in deep water or well below the surface.

Sleep, Norman H.↗

NASA NTRS2003

Sensor Calibration and Ocean Products for TRMM Microwave Radiometer

During the three years of finding, we have carefully corrected for two sensor/platform problems, developed a physically based retrieval algorithm to calculate SST, wind speed, water vapor, cloud liquid water and rain rates, validated these variables, and demonstrated that satellite microwave radiometers can provide very accurate SST retrievals through clouds. Prior to this, there was doubt by some scientists that the technique of microwave SST retrieval from satellites is a viable option. We think we have put these concerns to rest, and look forward to making microwave SSTs a standard component of the Earth science data sets. Our TMI SSTs were featured on several network news broadcasts and were reported in Science magazine. Additionally, we have developed a SST algorithm for VIRS to facilitate IR/MW inter-comparisons and completed research into diurnal cycles and air-sea interactions.

Wentz, Frank J.↗

NASA NTRS2004

Parameterization of the Vertical Variability of Tropical Cirrus Cloud Microphysical and Optical Properties

Cloud Integrating Nephelometers (CIN) were flown on the U. North Dakota Citation aircraft and the NASA WB-57 aircraft for the purpose of measuring in-situ the optical extinction coefficient and the asymmetry parameter (g) at a wavelength of 635 nm of primarily ice particles encountered during the NASA CRYSTAL-FACE study of large cumulus clouds (Cu) and their anvils found in the southern Florida region. The probes performance was largely successful and produced archived data for vertical profiles of extinction, asymmetry parameter, and effective radius (Re), the latter being obtained by combining CIN and CVI (total water; Oregon State U.) measurements. Composites of the CIN and CVI data describing the average microphysical and optical behavior of the Cu and their anvils showed the following: The extinction increases with height as a result of the size of the particles also decreasing with height as shown by the Re measurements; near the top of anvils the size of the primary ice particles is about 10-um radius; and the value of g does not vary significantly with height and has a mean value of about 0.73 consistent with the idea that ambient ice crystals are primarily of complex shape and reflect solar radiation more efficiently than particles of pristine crystal shape. Other observations include: The g measurements were found to be an indicator of the phase of the cloud permitting identification of the clouds with water droplets, rain, and ice; visual ranges as small as several tens of meters were occasionally found in "extinction cores" that coincided with strong updraft cores; and comparison of the cloud probes on the Citation showed significant disagreement.

Gerber, Hermann E.↗

NASA NTRS2003

Sensor Calibration and Ocean Products for TRMM Microwave Radiometer

During the three years of fundin& we have carefully corrected for two sensor/platform problems, developed a physically based retrieval algorithm to calculate SST, wind speed, water vapor, cloud liquid water and rain rates, validated these variables, and demonstrated that satellite microwave radiometers can provide very accurate SST retrievals through clouds. Prior to this, there was doubt by some scientists that the technique of microwave SST retrieval from satellites is a viable option. We think we have put these concerns to rest, and look forward to making microwave SSTs a standard component of the Earth science data sets. Our TMI SSTs were featured on several network news broadcasts and were reported in Science magazine. Additionally, we have developed a SST algorithm for VIRS to facilitate IR/MW inter-comparisons and completed research into diurnal cycles and air-sea interactions.

Lawrence, Richard J.↗

NASA NTRS2010

A Framework for the Generation and Dissemination of Drop Size Distribution (DSD) Characteristics Using Multiple Platforms

Proper characterization of the precipitation drop size distribution (DSD) is integral to providing realistic and accurate space- and ground-based precipitation retrievals. Current technology allows for the development of DSD products from a variety of platforms, including disdrometers, vertical profilers and dual-polarization radars. Up to now, however, the dissemination or availability of such products has been limited to individual sites and/or field campaigns, in a variety of formats, often using inconsistent algorithms for computing the integral DSD parameters, such as the median- and mass-weighted drop diameter, total number concentration, liquid water content, rain rate, etc. We propose to develop a framework for the generation and dissemination of DSD characteristic products using a unified structure, capable of handling the myriad collection of disdrometers, profilers, and dual-polarization radar data currently available and to be collected during several upcoming GPM Ground Validation field campaigns. This DSD super-structure paradigm is an adaptation of the radar super-structure developed for NASA s Radar Software Library (RSL) and RSL_in_IDL. The goal is to provide the DSD products in a well-documented format, most likely NetCDF, along with tools to ingest and analyze the products. In so doing, we can develop a robust archive of DSD products from multiple sites and platforms, which should greatly benefit the development and validation of precipitation retrieval algorithms for GPM and other precipitation missions. An outline of this proposed framework will be provided as well as a discussion of the algorithms used to calculate the DSD parameters.

Wolf, David B.↗

NASA NTRS1999

The Question of Future Droughts in a CO2-Warmed World

Increased droughts are to be expected in a warmer world, and so are increased floods. A warmer atmosphere can hold more moisture, and evaporate more water from the surface. Thus, when it is not raining, available soil water should be reduced. When it is raining, it could very well rain harder. Most researchers agree then that a warmer world will have greater hydrologic extremes. In addition, there is a basic imbalance that develops as climate warms, between the loss of moisture from the soil by evaporation and replenishment via precipitation. The land has a smaller heat capacity than the ocean, so it should warm faster. Evaporation from the land proceeds at the rate of its warming, while precipitation derives primarily from evaporation at the ocean surface. As the latter is increasing more slowly, in a warmer world, precipitation will not increase as rapidly as evaporation due to the fact that the oceans warm more slowly than the land surface (evaporation over the ocean is slower than over the land). Hence, more droughts are anticipated in a warmer world, but the specific location of such droughts is somewhat uncertain. To address the question of where droughts are likely to occur, one first needs to have a reasonable sense of what the future magnitude of warming will be, and what the latitudinal distribution of warming will be. For example, the greater the warming at high latitudes relative to low latitudes, the more likely there will be increased drought over the U.S. in summer. In contrast, substantial tropical warming could give us El Nino-like precipitation, with intensified flooding along the southern tier of the U.S. All of these conditions are likely to intensify as the global temperature rises.

Rind, David↗

NASA NTRS2012

Two Years of Site Diversity Measurements in Guam, USA

As NASA communication networks upgrade to higher frequencies, such as Ka-Band, atmospherically induced attenuation can become significant. This attenuation is caused by rain, clouds and atmospheric gases (oxygen and water vapor), with rain having the most noticeable effects. One technique to circumvent the increase in attenuation is to operate two terminals separated by a distance that exceeds the average rain cell size. The fact that rain cells are of finite size can then be exploited by rerouting the signal to the terminal with the strongest link. This technique, known as site diversity, is best suited for climates that have compact (less than 2km) and intense rain cells such as in Guam. In order to study the potential diversity gain at the Tracking and Data Relay Satellite (TDRS) Remote Ground Terminal (GRGT) complex in Guam a site test interferometer (STI) was installed in May of 2010. The STI is composed of two terminals with a 900m baseline that observe the same unmodulated beacon signal broadcast from a geostationary satellite (e.g., UFO 8). The potential site diversity gain is calculated by measuring the difference in signal attenuation seen at each terminal. Over the two years of data collection the cumulative distribution function (CDF) of the site diversity gain shows a better than 3 dB improvement for 90% of the time over standard operation. These results show that the use of site diversity in Guam can be very effective in combating rain fades.

Acosta, Roberto J.↗

NASA NTRS2006

Precipitation Measurements from Space: Why Do We Need Them?

Water is fundamental to the life on Earth and its phase transition between the gaseous, liquid, and solid states dominates the behavior of the weather/climate/ecological system. Precipitation, which converts atmospheric water vapor into rain and snow, is central to the global water cycle. It regulates the global energy balance through interactions with clouds and water vapor (the primary greenhouse gas), and also shapes global winds and dynamic transport through latent heat release. Surface precipitation affects soil moisture, ocean salinity, and land hydrology, thus linking fast atmospheric processes to the slower components of the climate system. Precipitation is also the primary source of freshwater in the world, which is facing an emerging freshwater crisis in many regions. Accurate and timely knowledge of global precipitation is essential for understanding the behavior of the global water cycle, improving freshwater management, and advancing predictive capabilities of high-impact weather events such as hurricanes, floods, droughts, and landslides. With limited rainfall networks on land and the impracticality of making extensive rainfall measurements over oceans, a comprehensive description of the space and time variability of global precipitation can only be achieved from the vantage point of space. This presentation will examine current capabilities in space-borne rainfall measurements, highlight scientific and practical benefits derived from these observations to date, and provide an overview of the multi-national Global Precipitation Measurement (GPM) Mission scheduled to be launched in the early next decade.

Hou, Arthur Y.↗

NASA NTRS2007

Precipitation Measurements from Space: The Global Precipitation Measurement Mission

Hou, Arthur Y.↗

DOE OSTI2025

Model Intercomparison of the Impacts of Varying Cloud Droplet–Nucleating Aerosols on the Life Cycle and Microphysics of Isolated Deep Convection

The microphysical impacts of aerosol particles on scattered isolated deep convective cells near Houston, Texas, on 19 June 2013, are examined using multiple cloud-resolving model (CRM) simulations initialized with vertical profiles of low and high concentrations of cloud droplet–nucleating aerosols. These simulations formed part of the Model Intercomparison Project (MIP) conducted by the Deep Convective Working Group of the Aerosol, Cloud, Precipitation and Climate (ACPC) initiative. Each CRM generated a field of convective cells representing those observed during the case study with varying degrees of accuracy. The Tracking and Object-Based Analysis of Clouds (tobac) cell-tracking algorithm was applied to each MIP CRM simulation to track relatively long-lived convective cells (20–60 min). Most of the CRMs produced similar aerosol loading impacts on the warm phase of tracked cell properties with reduced autoconversion and accretion growth of rain, increased cloud water, reduced rainfall, and reduced near-surface evaporation of rain. The sign of aerosol impacts on the warm-phase properties of the convective cells was also quite consistent over cell lifetimes with the greatest magnitude of influence in the first half of the life cycle in most CRMs. In contrast, the ice-phase response to aerosol loading was highly variable among CRMs and included increases or decreases in ice amounts at inconsistent stages of the cell life cycle and midlevel versus upper-level changes in ice. This intermodel variability in ice is indicative both of the complex indirect interactions between aerosols and ice-phase processes in deep convection and their associated parameterizations.

Aerosol-cloud interaction↗

DOE OSTI2025

Widespread underestimation of rain-induced soil carbon emissions from global drylands

Dryland carbon fluxes, particularly those driven by ecosystem respiration, are highly sensitive to water availability and rain pulses. However, the magnitude of rain-induced carbon emissions remains unclear globally. Here we quantify the impact of rain-pulse events on the carbon balance of global drylands and characterize their spatiotemporal controls. Using eddy-covariance observations of carbon, water and energy fluxes from 34 dryland sites worldwide, we produce an inventory of over 1,800 manually identified rain-induced CO2 pulse events. Based on this inventory, a machine learning algorithm is developed to automatically detect rain-induced CO2 pulse events. Our findings show that existing partitioning methods underestimate ecosystem respiration and photosynthesis by up to 30% during rain-pulse events, which annually contribute 16.9 ± 2.8% of ecosystem respiration and 9.6 ± 2.2% of net ecosystem productivity. We show that the carbon loss intensity correlates most strongly with annual productivity, aridity and soil pH. Finally, we identify a universal decay rate of rain-induced CO2 pulses and use it to bias-correct respiration estimates. Our research highlights the importance of rain-induced carbon emissions for the carbon balance of global drylands and suggests that ecosystem models may largely underrepresent the influence of rain pulses on the carbon cycle of drylands.

Nguyen, Ngoc B↗

DOE OSTI2024

Energy‐Water Asynchrony Principally Determines Water Available for Runoff From Snowmelt in Continental Montane Forests

ABSTRACT Changes in the volume, rate, and timing of the snowmelt water pulse have profound implications for seasonal soil moisture, evapotranspiration (ET), groundwater recharge, and downstream water availability, especially in the context of climate change. Here, we present an empirical analysis of water available for runoff using five eddy covariance towers located in continental montane forests across a regional gradient of snow depth, precipitation seasonality, and aridity. We specifically investigated how energy‐water asynchrony (i.e., snowmelt timing relative to atmospheric demand), surface water input intensity (rain and snowmelt), and observed winter ET (winter AET) impact multiple water balance metrics that determine water available for runoff (WAfR). Overall, we found that WAfR had the strongest relationship with energy‐water asynchrony (adjusted r 2 = 0.52) and that winter AET was correlated to total water year evapotranspiration but not to other water balance metrics. Stepwise regression analysis demonstrated that none of the tested mechanisms were strongly related to the Budyko‐type runoff anomaly (highest adjusted r 2 = 0.21). We, therefore, conclude that WAfR from continental montane forests is most sensitive to the degree of energy‐water asynchrony that occurs. The results of this empirical study identify the physical mechanisms driving variability of WAfR in continental montane forests and are thus broadly relevant to the hydrologic management and modelling communities.

Webb, Ryan William↗