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Improving Ocean Reanalyses and ENSO Forecasts By Assimilation of Rain Corrected Satellite Sea Surface Salinity Using the GMAO S2S Forecast System

During the past years, we have seen that the La Nina to El Nino transition has had a significant global socio-economic impact and so has been the key focus for improving coupled ocean-atmosphere forecasts. Assimilation of satellite altimetry and subsurface temperature and salinity from (mostly) Argo help to improve the initialization of the thermocline, while satellite Sea Surface Temperature (SST) aids in constraining surface heat-fluxes, leading to improved subseasonal to seasonal forecasts of the coupled system. However, few studies have focused on improving the fresh-water flux and near-surface density and mixing through assimilation of satellite sea surface salinity (SSS). For expediency, the few ocean models that do assimilate SSS bias-correct the satellite SSS data to normalize towards the near-surface Argo data. However, in rainy regions, where buoyant water sits as a fresh lens at the surface, this assumption is likely inadequate. In previous work, we have shown that adjusting SSS data to bulk salinity (Sb) using the Rain Impact Model (RIM) of Santos-Garcia et al., 2014 has improved the near-surface density and mixed layer depth, leading to deeper thermocline and improved the NINO3.4 SST forecasts. Now we utilize the Soil Moisture/Ocean Salinity, Rain Corrected (SMOS_RC) SSS product provided by the Centre Aval de Traitement des données SMOS (CATDS CPDC) to represent the Sb more accurately at first model layer (in our case 5 m). Rather than using a diffusivity model as with RIM, SMOS_RC relies on an observed relationship between the spatial heterogeneity of SMOS SSS and instantaneous rain rate (RR) (Supply et al., 2020). In order to test the impact of SMOS_RC versus SMOS, we compare two reanalyses over the period 2014 to 2021. For both reanalysis experiments, all available along-track absolute dynamic topography and in situ observations are assimilated using the LETKF scheme (Penny et al., 2013). One reanalysis additionally assimilates SMOS SSS data as is (i.e., with the fresh bias), and a separate reanalysis is performed assimilating the SMOS_RC data. We assess the impact for near-surface and subsurface dynamics within ocean reanalyses by validating against observations and explore how SSS assimilation (SMOS versus SMOS_RC) impacts dynamical ENSO forecasts using the NASA GMAO Sub-seasonal to Seasonal coupled forecast system (GEOS S2S-3, Molod et al., 2020, Hackert et al., 2023). We will show that improved SSS estimates and near-surface density and mixing led to more accurate coupled air/sea interaction and better ENSO forecasts.

Eric Hackert↗

NASA NTRS1998

The NASDA Global Rain Forest Mapping (GRFM) Project Releases First CD-ROM

The goal of the Global Rain Forest Mapping project (GRFM) is to acquire continuous, high resolution L-band Synthetic Aperture Radar (SAR) imagery of the major tropical rain forests of the world using the Japanese Earth Resources Satellite (JERS-1) SAR.

rain forest mapping SAR Global Rain Forest Mapping↗

DOE OSTI2018

Global patterns of extreme drought-induced loss in land primary production: Identifying ecological extremes from rain-use efficiency

Quantifying the ecological patterns of loss of ecosystem function in extreme drought is important to understand the carbon exchange between the land and atmosphere. Rain-use efficiency [RUE; gross primary production (GPP)/precipitation] acts as a typical indicator of ecosystem function. Here in this study, a novel method based on maximum rain-use efficiency (RUE max ) was developed to detect losses of ecosystem function globally. Three global GPP datasets from the MODIS remote sensing data (MOD17), ground upscaling FLUXNET observations (MPI-BGC), and process-based model simulations (BESS), and a global gridded precipitation product (CRU) were used to develop annual global RUE datasets for 2001–2011. Large, well-known extreme drought events were detected, e.g. 2003 drought in Europe, 2002 and 2011 drought in the U.S., and 2010 drought in Russia. Our results show that extreme drought-induced loss of ecosystem function could impact 0.9% ± 0.1% of earth's vegetated land per year and was mainly distributed in semi-arid regions. The reduced carbon uptake caused by functional loss (0.14 ± 0.03 PgC/yr) could explain >70% of the interannual variation in GPP in drought-affected areas (p ≤ 0.001). Our results highlight the impact of ecosystem function loss in semi-arid regions with increasing precipitation variability and dry land expansion expected in the future.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

Radar Derived Rainfall and Rain Gauge Measurements at SRS

Over the years rainfall data for the Savannah River Site has been obtained from ground level measurements made by rain gauges. These instruments have inherent errors or biases that can impact the measured rainfall totals but are assumed as ground truth for most climatological and weather applications. With the development of weather radar technologies, various methods to derive rainfall totals from radar reflectivity values have been developed and have continued to improve. The Z-R relationship, which uses an exponential relationship to estimate rainfall rate based on radar reflectivity values, provides estimates of rainfall amounts (Z-R Level III) for locations within the radar’s detection range. The recently developed Multi-Radar Multi-Sensor (MRMS) dataset combines reflectivity-based estimates using the Z-R relationship with a network of gauges and other rainfall estimates to produce a refined set of precipitation estimates for each grid point within its domain. Comparisons were done between gauge observations and radar estimates for various SRS locations to assess whether radar derived estimates are representative of rainfall measurements at the site. Results obtained show good agreement between radar derived amounts and ground measurements, with MRMS showing stronger correlations and lower spread than Z-R Level III estimates. Outliers and errors observed appear to be related to hydrometeor classification schemes. In general, MRMS proved to provide sufficiently representative estimates of daily rainfall amounts to replace existing SRS rain gauge measurements.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2018

GoAmazon2014/5 Rain Rates from the SIPAM Manaus S-band Radar

Two years of volumetric radar observations acquired during the GoAmazon2014/5 field campaign by the SIPAM S-band radar in Manaus, Brazil were used to create rain rate estimates from radar reflectivity by Texas A&M University. Estimates of rain rates were generated using radar reflectivity data from the 2.5 km level and a Z-R relationship of Z=174.8*R^1.56.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS1997

The TRMM 'Day-1' Radar/Radiometer Combined Rain-Profiling Algorithm

The Tropical Rainfall Measuring Mission (TRMM)'s 'day-1' combined rada/radiometer algorithm uses a rain-profiling approach which gives as much importance to the measurement of the TRMM satellite's precipitation radar (PR) and the TRMM microwave imager (TMI) as their respective intrinsic ambiguities warrant, which avoids any ad hoc shortcuts that might introduce large biases in the rain estimates, yet which is simple enough to be operatrional with TRMM is launched in late 1997.

Rain-Profiling Algorithm Tropical Rainfall↗

NASA NTRS1993

Optimal Estimation of Rain Rate Profiles From Radar Returns at Attenuating Wavelengths

There are significant inherent ambiguities when one tries to determine a particular vertical rain intensity profile from a given time profile of radar echo powers measured by a downward-looking (spaceborne or airborne) radar at a single attenuating frequency. In this paper, we quantify these ambiguities mathematically, and examine their effects on the performance of rain-rate retrieval algorithms initially proposed for use by the Precipitation Radar of the Tropical Rainfall Measuring Mission (TRMM).

vertical rain intensity profiles spaceborne radar ↗

NASA NTRS2013

Evaluating Light Rain Drop Size Estimates from Multiwavelength Micropulse Lidar Network Profiling

This paper investigates multiwavelength retrievals of median equivolumetric drop diameter D(sub 0) suitable for drizzle and light rain, through collocated 355-/527-nm Micropulse Lidar Network (MPLNET) observations collected during precipitation occurring 9 May 2012 at the Goddard Space Flight Center (GSFC) project site. By applying a previously developed retrieval technique for infrared bands, the method exploits the differential backscatter by liquid water at 355 and 527 nm for water drops larger than approximately 50 micrometers. In the absence of molecular and aerosol scattering and neglecting any transmission losses, the ratio of the backscattering profiles at the two wavelengths (355 and 527 nm), measured from light rain below the cloud melting layer, can be described as a color ratio, which is directly related to D(sub 0). The uncertainty associated with this method is related to the unknown shape of the drop size spectrum and to the measurement error. Molecular and aerosol scattering contributions and relative transmission losses due to the various atmospheric constituents should be evaluated to derive D(sub 0) from the observed color ratio profiles. This process is responsible for increasing the uncertainty in the retrieval. Multiple scattering, especially for UV lidar, is another source of error, but it exhibits lower overall uncertainty with respect to other identified error sources. It is found that the total error upper limit on D(sub 0) approaches 50%. The impact of this retrieval for long-term MPLNET monitoring and its global data archive is discussed.

light-rain drop size estimates↗

NASA NTRS2018

The Global Precipitation Measurement (GPM) Mission's Scientific Achievements and Societal Contributions: Reviewing Four Years of Advanced Rain and Snow Observations

Precipitation represents a life-critical energy and hydrologic exchange between the Earth’s atmosphere and its surface. As such, knowledge of where, when, and how much rain and snow falls is essential for scientific research and societal applications. Building on the 17-year success of the Tropical Rainfall Measurement Mission (TRMM), the Global Precipitation Measurement (GPM) Core Observatory (GPM-CO) is the first U.S. National Aeronautical and Space Administration (NASA) satellite mission specifically designed with sensors to observe the structure and intensities of both rain and falling snow. The GPM-CO has proved to be a worthy successor to TRMM, extending and improving high-quality active and passive microwave observations across all times of day. The GPM-CO launched in early 2014, is a joint mission between NASA and the Japanese Aerospace Exploration Agency (JAXA), with sensors that include the NASA-provided GPM Microwave Imager and the JAXA-provided Dual-frequency Precipitation Radar. These sensors were devised with high accuracy standards enabling them to be used as a reference for inter-calibrating a constellation of partner satellite data. These intercalibrated partner satellite retrievals are used with infrared data to produce merged precipitation estimates at temporal scales of 30 minutes and spatial scales of 0.1° x 0.1°. Precipitation estimates from the GPM-CO and partner constellation satellites, provided in near real time and later reprocessed with all ancillary data, are an indispensable source of precipitation data for operational and scientific users. Advances have been made using GPM data, primarily in improving sensor calibration, retrieval algorithms, and ground validation measurements, and used to further our understanding of the characteristics of liquid and frozen precipitation and the science of water and hydrological cycles for climate/weather forecasting. These advances have extended to societal benefits related to water resources, operational numerical weather prediction, hurricane monitoring, prediction, and disaster response, extremes, and disease.

Applications↗

NASA NTRS2017

Comparison of Satellite Rainfall Estimates and Rain Gauge Measurements in Italy, and Impact on Landslide Modeling

Landslides can be triggered by intense or prolonged rainfall. Rain gauge measurements are commonly used to predict landslides even if satellite rainfall estimates are available. Recent research focuses on the comparison of satellite estimates and gauge measurements. The rain gauge data from the Italian network (collected in the system database Verifica Rischio Frana" (VRF)) are compared with the National Aeronautics and Space Administration (NASA) Tropical Rainfall Measuring Mission (TRMM) products. For the purpose, we couple point gauge and satellite rainfall estimates at individual grid cells, evaluating the correlation between gauge and satellite data in different morpho-climatological conditions. We then analyze the statistical distributions of both rainfall data types and the rainfall events derived from them. Results show that satellite data underestimates ground data, with the largest differences in mountainous areas. Power-law models, are more appropriate to correlate gauge and satellite data. The gauge and satellite-based products exhibit different statistical distributions and the rainfall events derived from them differ. In conclusion, satellite rainfall cannot be directly compared with ground data, requiring local investigation to account for specific morpho-climatological settings. Results suggest that satellite data can be used for forecasting landslides, only performing a local scaling between satellite and ground data.

landslide; satellite rainfall; TRMM; rain gauges; ↗

NASA NTRS1996

SIR-C/X-SAR Observations of Rain Storms

Teh SIR-C/X-sar radar observations of rain storms are the first multi-polarization and multi-frequency observations of percipitation from space. In addition to numerous, often dramatic images of severe weather systems obtained by forming a synthetic aperture in the usual side-looking attitude, several data takes were performed while the radar antennas were parallel to the ground and the radar beams were pointing at nadir. These oppoprtunities coincided with the passage of the Shuttle over Tropical Cyclone Odille in the southern Indian Ocean during the first flight, and over Typhoon Seth in the Western Pacific during the second flight. The resulting observations, or more apropriately, the resulting measurements, demonstrate for the first time the capability of a spaceborne multi-frequency multi-polarization microwave radar system to quantify precipitation rates, to detect hydrometer phase, and to classify rain type.

SIR-C↗

NASA NTRS2021

Assessing the Impact of Light/Shallow Precipitation Retrievals from Satellite-Based Observations Using Surface Radar and Micro Rain Radar Observations

The accurate representation of precipitation across the Earth’s surface is crucial to furthering our knowledge and understanding of the Earth System and its component processes. Precipitation poses a number of challenges, particularly due to the variability of precipitation over time and space and whether it falls as snow or rain. While conventional measures of precipitation are reasonably good at the location of their measurement, their distribution across the Earth’s surface is uneven with some regions having no surface measurements. Spaceborne sensors have the capability of providing regular observations across the Earth’s surface that can provide estimates of precipitation. However, the estimation of precipitation from satellite observations is not necessarily straightforward. Visible and/or infrared techniques rely upon imprecise cloud-top to surface precipitation relationships, while the sensitivity of passive microwave techniques to different precipitation types is not consistent. Active microwave (radar) observations provide the most direct satellite measurements of precipitation but cannot provide estimates close to the surface and are generally not sufficiently sensitive to resolve light precipitation. This is particularly problematic at mid to high latitudes, where light and/or shallow precipitation dominates. This paper compares measurements made by ground-based weather radars, Micro Rain Radars and the spaceborne Dual-frequency Precipitation Radar to study both light precipitation intensity and shallow precipitation occurrence and to assess their impact on satellites retrievals of precipitation at the mid to high latitudes.

light precipitation↗

DOE OSTI2016

Rain evaporation and moist patches in tropical boundary layers

Moist patches are areas in the subcloud layer characterized by a positive water vapor anomaly compared to the environment and are considered important in triggering new convective cells. A correct understanding of the origin of the water vapor in these patches is, thus, essential to improving existing convective parameterizations. Recent studies have addressed this problem and have shown that contrary to what was previously thought, the main source of water vapor in moist patches are surface latent heat fluxes, instead of rain evaporation. This paper offers a different perspective to the topic, focusing on the origin only of the water vapor that makes moist patches anomalously moist when compared to the environment. Finally, it is found that near the surface, rain evaporation contributes half as much as latent heat fluxes, implying that a parameterization of the thermodynamic forcing should be more sensitive to environmental variables, like relative humidity, than recently suggested.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2016

Investigation of liquid cloud microphysical properties of deep convective systems: 1. Parameterization raindrop size distribution and its application for stratiform rain estimation

Abstract To investigate liquid‐phase ( T > 3°C) cloud and precipitation microphysical properties within Deep Convective Systems (DCSs), eight DCS cases sampled by the University of North Dakota Citation II research aircraft during Midlatitude Continental Convective Clouds Experiment were selected. A full spectrum of raindrop size distribution (DSD) was constructed from 120 µm to 4000 µm through a combination of two‐dimensional cloud probe (120 to 900 µm) and High Volume Precipitation Spectrometer (900 to 4000 µm) data sets. A total of 1126 five second DSDs have been used to fit to Gamma and Exponential functions within the stratiform rain (SR) regions of DCSs. The Gamma shape μ Γ and slope λ Γ parameters are then compared with those derived from surface disdrometer measurements. The similar μ Γ ‐ λ Γ relationships but different μ Γ and λ Γ value ranges from two independent platforms at different elevations may represent the real nature of DSD shape information in clouds and at the surface. To apply the exponentially fitted DSD parameters to precipitation estimation using Next Generation Weather Radar (NEXRAD) radar reflectivity factor Z e , the terms N 0 E and λ E have been parameterized as a function of Z e using an empirical N 0 E ‐ λ E relationship. The averaged SR rain rate retrieved from this study is almost identical to the surface measurements, while the NEXRAD Q2 precipitation is twice as large. The comparisons indicate that the new DSD parameterization scheme is robust, while the Q2 SR precipitation estimation based on Marshall‐Palmer Z‐R relationship, where a constant DSD intercept parameter ( N 0 E ) was assumed, needs to be improved for heavy precipitation cases.

Wang, Jingyu↗

DOE OSTI2016

The tropical rain belts with an annual cycle and a continent model intercomparison project: TRACMIP

This paper introduces the Tropical Rain belts with an Annual cycle and a Continent Model Intercomparison Project (TRACMIP). TRACMIP studies the dynamics of tropical rain belts and their response to past and future radiative forcings through simulations with 13 comprehensive and one simplified atmosphere models coupled to a slab ocean and driven by seasonally-varying insolation. Five idealized experiments, two with an aquaplanet setup and three with a setup with an idealized tropical continent, fill the space between prescribed-SST aquaplanet simulations and realistic simulations provided by CMIP5/6. The simulations reproduce key features of the present-day climate and expected future climate change, including an annual-mean intertropical convergence zone (ITCZ) that is located north of the equator and Hadley cells and eddy-driven jets that are similar to the present-day climate. Quadrupling CO 2 leads to a northward ITCZ shift and preferential warming in Northern high-latitudes. The simulations show interesting CO 2 -induced changes in the seasonal excursion of the ITCZ and indicate a possible state-dependence of climate sensitivity. The inclusion of an idealized continent modulates both the control climate and the response to increased CO 2 ; for example it reduces the northward ITCZ shift associated with warming and, in some models, climate sensitivity. In response to eccentricity-driven seasonal insolation changes, seasonal changes in oceanic rainfall are best characterized as a meridional dipole, while seasonal continental rainfall changes tend to be symmetric about the equator. Finally, this survey illustrates TRACMIP’s potential to engender a deeper understanding of global and regional climate phenomena and to address pressing questions on past and future climate change.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2017

Terrestrial acidification and ecosystem services: effects of acid rain on bunnies, baseball, and Christmas trees

Often termed “acid rain,” combined nitrogen and sulfur deposition can directly and indirectly impact the condition and health of forest ecosystems. Researchers use critical loads (CLs) to describe response thresholds, and recent studies on acid-sensitive biological indicators show that forests continue to be at risk from terrestrial acidification. However, rarely are impacts translated into changes in “ecosystem services” that impact human well-being. Further, the relevance of this research to the general public is seldom communicated in terms that can motivate action to protect valuable resources. To understand how changes in biological indicators affect human well-being, we used the STEPS (Stressor–Ecological Production function–final ecosystem Services) Framework to quantitatively and qualitatively link CL exceedances to ecosystem service impacts. We specified the cause-and-effect ecological processes linking changes in biological indicators to final ecosystem services. The Final Ecosystem Goods and Services Classification System (FEGS-CS) was used within the STEPS Framework to classify the ecosystem component and the beneficiary class that uses or values the component. We analyzed two acid-sensitive tree species, balsam fir (Abies balsamea) and white ash (Fraxinus americana), that are common in northeastern USA. These well-known species provide habitat for animals and popular forest products that are relatable to a broad audience. We identified 160 chains with 10 classes of human beneficiaries for balsam fir and white ash combined, concluding that there are resources at risk that the public may value. Two stories resulting from these explorations into the cascading effects of acid rain on terrestrial resources are ideal for effective science communication: the relationship between (1) balsam fir as a popular Christmas tree and habitat for the snowshoe hare, a favorite of wildlife viewers, and (2) white ash because it is used for half of all baseball bats, fine wood products, and musical instruments. Thus, rather than focusing on biological indicators that may only be understood or appreciated by specific stakeholders or experts, this approach extends the analysis to include impacts on FEGS and humans. It also lays the foundation for developing stakeholder-specific narratives, quantitative measures of endpoints, and for conducting demand-based valuations of affected ecosystem services.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2018

The Influence of Aerosols and Meteorological Conditions on the Total Rain Volume of the Mesoscale Convective Systems Over Tropical Continents

This study provides an observational assessment of the variations of the total rain volume (TRV) with aerosols through the entire lifetime of mesoscale convective systems (MCSs) over tropics. Using 70,000 MCSs' samples, we show that TRV increases with aerosols from clean to moderately heavy polluted conditions (aerosol optical depth [AOD] ~ 0.0–0.4). TRV decreases when AOD exceeds 0.5. The TRV change with AOD is strongest under favorable meteorological conditions, such as high total precipitable water (45–75 kg/m 2 ), high convective available potential energy (1,200–2,400 J/kg), and intermediate vertical wind shear (9–21 × 10 -4 /s). TRV of MCSs increases from 2 to 4 km 3 (rain depth ~ 20–40 mm) when AOD < 0.15 or >0.5 , to more than 12 km 3 (~120 mm) when 0.2 < AOD < 0.4 under above the mentioned optimal meteorological conditions. The basic response of TRV to aerosol concentrations is similar under all the meteorological conditions and during all stages of the MCS lifecycle..

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2019

Impacts of Local Convective Processes on Rain on the Caribbean Island of Puerto Rico

Abstract This study aims to determine the impacts of tropical island processes on local convective storms. An analysis of rain events on the island of Puerto Rico between 1 June 2015 and 31 July 2016 showed that local island‐enhanced western storms accounted for 89 of 322 storms. This period is of particular importance for the Caribbean as 2015 was one of the driest years on record. While large‐scale influences such as the El Niño–Southern Oscillation, the North Atlantic Oscillation, African easterly waves, and Saharan dust transport modulate moisture conditions in the region, correlations between precipitation and El Niño–Southern Oscillation (−0.14), North Atlantic Oscillation (−0.42), and Saharan dust (0.1) for 1980–2016 ranged from weak to moderate. Local data for the island of Puerto Rico from weather stations, the Convection, Aerosol, and Synoptic‐Effects in the Tropics field campaign, and the North American Mesoscale model support the initiation or enhancement of convective rain events due to local island processes. In particular, analysis of surface wind speed/direction, convective available potential energy, lifted index, and the bulk Richardson number substantiate local instability due to surface heating, orographic uplift, and sea breeze trade‐wind convergence. These convective forcings along with available precipitable water in excess of 50 mm ultimately led to intense storms despite severe rainfall‐mitigating dust episodes for which aerosol optical thickness exceeded 0.4. These results may have major implications for considering the impacts of local air‐sea‐land interactions on rainfall over other tropical islands.

Hosannah, N.↗