Contribution of Meteorological Downscaling to Skill and Precision of Seasonal Drought Forecasts

Research in meteorological prediction on sub-seasonal to seasonal (S2S) timescales has seen growth in recent years. Concurrent with this, demand for seasonal drought forecasting has risen. While there is obvious synergy between these fields, S2S meteorological forecasting has typically focused on low resolution global models, while the development of drought can be sensitive to the local expression of weather anomalies and their interaction with local surface properties and processes. This suggests that downscaling might play an important role in the application of meteorological S2S forecasts to skillful forecasting of drought. Here, we apply the Generalized Analog Regression Downscaling (GARD) algorithm to downscale meteorological hindcasts from the NASA Goddard Earth Observing System (GEOS) global S2S forecast system. Downscaled meteorological fields are then applied to drive offline simulations with the Catchment Land Surface Model (CLSM) to forecast United States Drought Monitor (USDM) style drought indicators derived from simulated surface hydrology variables. We compare the representation of drought in these downscaled hindcasts to hindcasts that are not downscaled, using the North American Land Data Assimilation System Phase 2 (NLDAS-2) dataset as an observational reference. We find that downscaling using GARD improves hindcasts of temperature and temperature anomalies, but the results for precipitation are mixed and generally small. Overall, GARD downscaling led to improved hindcast skill for total drought across the Contiguous United States (CONUS), and improvements were greatest for extreme (D3) and exceptional (D4) drought categories.

In situ observations of stratospheric HCl using three-mirror integrated cavity output spectroscopy

Stratospheric HCl observations are an important diagnostic for the evaluation of catalytic processes that impact the ozone layer. We report here in situ balloon-borne observations of HCl employing an off-axis integrated cavity output spectrometer (OA-ICOS) fitted with a reinjection mirror. Laboratory assessments demonstrated that the spectrometer has a 90 % response time of 10 s to changes in HCl and a 30 s precision of 26 pptv. The instrument was deployed alongside an ozone instrument in August 2018 on a balloon-borne descent between 20–80 hPa (29–18 km altitude). The observations agreed with nearby satellite measurements made by the Earth Observing System Microwave Limb Sounder within 10 % on average. This is the first time that stratospheric measurements of HCl have been made with ICOS and the first time any cavity-enhanced HCl instrument has been tested in flight.

Evaluation of GEOS Precipitation Flagging for SMAP Soil Moisture Retrieval Accuracy

The precipitation flag in the Soil Moisture Active Passive (SMAP) Level 2 passive soil moisture (L2SMP) retrieval product indicates the presence or absence of heavy precipitation at the time of the SMAP overpass. The flag is based on precipitation estimates from the Goddard Earth Observing System (GEOS) Forward Processing numerical weather prediction system. An error in flagging during an active or recent precipitation event can either (1) produce an overestimation of soil moisture due to short-term surface wetting of vegetation and/or surface ponding (if soil moisture retrieval was attempted in the presence of rain), or (2) produce an unnecessary non-retrieval of soil moisture and loss of data (if retrieval is flagged due to an erroneous indication of rain). Satellite precipitation estimates from the Integrated Multi-satellite Retrievals for GPM (IMERG) Version 06 Early Run (latency of ~4 hrs) precipitationCal product are used here to evaluate the GEOS-based precipitation flag in the L2SMP product for both the 6 PM ascending and 6 AM descending SMAP overpasses over the first five years of the mission (2015-2020). Consisting of blended precipitation measurements from the GPM (Global Precipitation Mission) satellite constellation, IMERG is treated as the “truth” when comparing to the GEOS model forecasts of precipitation used by SMAP. Key results include: i) IMERG measurements generally show higher spatial variability than the GEOS forecast precipitation, ii) the IMERG product has a higher frequency of light precipitation amounts, and iii) the effect of incorporating IMERG rainfall measurements in lieu of GEOS precipitation forecasts are minimal on the L2SMP retrieval accuracy (determined vs. in situ soil moisture measurements at core validation sites). Our results indicate that L2SMP retrievals continue to meet the mission’s accuracy requirement (standard deviation of the ubRMSE less than 0.04 m3/m3).

Overcoming the challenges of increasing resolution and complexity in GEOS

<p>The NASA Global Earth Observing System (GEOS) model supports an array of complex Earth system simulation and assimilation capabilities.<span>  </span>These range from simple development frameworks such as dry atmosphere dynamics and single column physics cases, to fully coupled atmosphere-ocean-land-cryosphere-chemistry. Efficient use of available computational resources requires extensive scientific development within each of these components, and optimized frameworks for coupling and executing these components in a comprehensive manner.<span>  </span>Ultimately, experiment design requires a compromise between complexity and increased resolution.<span>  </span>This talk will explore these compromises within the array of global DYAMOND Phase II winter 40-day simulations completed with GEOS. These include: 1) A coupled 4km ocean and 6km atmosphere with interactive two-moment aerosol cloud microphysics. 2) A 3km 181-level atmosphere with single-moment 6-phase cloud microphysics including 1km global carbon emissions for chemistry transport. 3) A 1.5km 181-level atmosphere with simple parameterized chemistry.</p>

The GMAO High‐Resolution Coupled Model and Assimilation System for Seasonal Prediction

<p>The Global Modeling and Assimilation Office (GMAO) is about to release a new version of the Goddard Earth Observing System (GEOS) Subseasonal to Seasonal prediction (S2S) system, GEOS‐S2S‐3, that represents an improvement in performance and infrastructure over the  previous system, GEOS-S2S-2. The system will be described briefly, highlighting some features unique to GEOS-S2S, such as the coupled interactive aerosol model and ensemble  perturbation strategy and size. Results are presented from forecasts and from climate  equillibrium simulations. GEOS-S2S-3 will be used to produce a long term weakly coupled reanalysis called MERRA-2 Ocean.</p><p>The climate or equillibrium state of the atmosphere and ocean shows a reduction in systematic error relative to GEOS‐S2S‐2, attributed in part to an increase in ocean resolution and to the upgrade in the glacier runoff scheme.  The forecast skill shows improved prediction  of the North Atlantic Oscillation, attributed to the increase in forecast ensemble members.  </p><p>With the release of GEOS-S2S-3 and MERRA-2 Ocean, GMAO will continue its tradition of maintaining a state‐of‐the‐art seasonal prediction system for use in evaluating the impact on seasonal and decadal forecasts of assimilating newly available satellite observations, as well as evaluating additional sources of predictability in the Earth system through the expanded coupling of the Earth system model and assimilation components.</p>

Toward a Climate and Calibration Observatory in space: NASA CLARREO Pathfinder and ESA TRUTHS

<p>The number, range and criticality of applications of Earth viewing optical sensors is increasing rapidly.  Not only from national/international space agencies but also through the launch of commercial constellations such as those of planet and the concept of Analysis Ready Data (ARD) reducing the skill needed for utilisation of the data.  However, no one organisation can provide all the tools necessary, and the need for a coordinated holistic earth observing system has never been greater. Achieving this vision has led to international initiatives coordinated by bodies such as the Committee on Earth Observation Satellites (CEOS and Global Space Inter-Calibration System (GISCS) of WMO to establish strategies to facilitate interoperability and the understanding and removal of bias through post-launch Calibration and Validation. </p><p>In parallel, the societal challenge resulting from climate change has been a major stimulus for significantly improved accuracy and trust of satellite data. Instrumental biases and uncertainty must be sufficiently small to minimise the multi-decadal timescales needed to detect small trends and attribute their cause, enabling them to become unequivocally accepted as evidence. </p><p>Although there have been many advances in the pre-flight SI-traceable calibration of optical sensors, in the last decade, unpredictable degradation in performance from both launch and operational environment remains a major difficulty.  Even with on-board calibration systems, uncertainties of less than a few percent are rarely achieved and maintained and the evidential link to SI-traceability is weak. For many climate observations the target uncertainty needs to be improved ten-fold. </p><p>However, this decade will hopefully see the launch of two missions providing spectrally resolved observations of the Earth at optical wavelengths, CLARREO Pathfinder on the International Space Station from NASA [1] and TRUTHS from ESA [2] to change this paradigm.  Both payloads are explicitly designed to achieve uncertainties close to the ideal observing system, commensurate with the needs of climate, with robust SI-Traceability evidenced in space.  Not only can they make high accuracy climate quality observations of the Earth and in the case of TRUTHS also the Sun, but they will also transfer their SI-traceable uncertainty to other sensors.  In this way creating the concept of a ‘metrology laboratory in space’, providing a ‘gold standard’ reference to anchor and improve the calibration of other sensors. The two missions achieve their traceability in orbit through differing methods but will use synergistic approaches for establishing in-flight cross-calibrations.  This paper will describe these strategies and illustrate the benefit through examples where improved accuracy has the most impact on the Earth observing system.</p><p>The complementarity and international value of these missions has ensured a strong partnership during early development phases of the full CLARREO mission and that of the NPL conceived TRUTHS. Following a proposal by the UK Space Agency  and subsequent adoption into the ESA EarthWatch program this partnership is further strengthened with the ESA team and a vision that together the two missions can lay the foundation of a framework for a future sustainable international climate and calibration observatory to the benefit of the global Earth Observing community.</p><p>References</p><p>[1]  https://clarreo-pathfinder.larc.nasa.gov/</p><p>[2] https://www.npl.co.uk/earth-observation/truths</p>

Calculating the Height and the Position of Volcanic Cloud SO2 With a Lagrangian Trajectory Tool

<p>We have developed a new trajectory tool to reconstruct the altitude and the position of SO<sub>2</sub> in a volcanic plume. Starting with 2D map of satellite observed SO<sub>2</sub>, known volcano location, and reanalysis wind fields from the NASA Goddard Earth Observing System (GEOS) model, the Goddard trajectory tool allows us to estimate the altitude and concentration of SO<sub>2</sub> in the volcanic plume at time of observation. We used this tool for the June 21, 2019 Mt. Raikoke eruption and the June 15, 1991 Mt. Pinatubo event. We used SO<sub>2</sub> data from the Ozone Mapping and Profiler Suite/Nadir Mapper (OMPS/NM) onboard the NASA-NOAA Suomi satellite and obtained a distribution of SO<sub>2</sub> altitudes between 1 and 19 kilometers in different parts of the Raikoke SO<sub>2</sub> clouds, with the highest SO<sub>2</sub> concentration between 11 and 16 km, in good agreement with data from independent SO<sub>2</sub> layer height retrievals from the Ozone Monitoring Instrument (OMI) aboard the NASA Aura spacecraft; the Tropospheric Monitoring Instrument (TROPOMI) onboard the European Copernicus Sentinel 5 precursor satellite and Infrared Atmospheric Sounding Interferometer (IASI) on the European Space Agency's (ESA) MetOp series of a polar orbiting satellites. We then applied this method to the Pinatubo eruption using SO<sub>2</sub> column measurements from the NASA Total Ozone Mapping Spectrometer (TOMS) and using wind fields from the National Centers for Environmental Prediction Reanalysis version 2. We found that the southern part of the Pinatubo plume is located in the troposphere, and the northern part is in the stratosphere.</p>

Progress toward Cloud-Cleared Infrared radiance assimilation in a global modeling framework: Application to the 2017 Atlantic Tropical Cyclone Season.

<p>Previous work by this team has demonstrated that assimilation of IR radiances in partially cloudy regions is beneficial to numerical weather predictions (NWPs), improving the representation of tropical cyclones (TCs) in global analyses and forecasts. The specific technique used by this team is based on the “cloud-clearing CC” methodology. Cloud-cleared hyperspectral IR radiances (CCRs), if thinned more aggressively than clear-sky radiances, have shown a strong impact on the analyzed representation and structure of TCs. However, the use of CCRs in an operational context is limited by 1) latency; and 2) external dependencies present in the original cloud-clearing algorithm. In this study, the Atmospheric InfraRed Sounder (AIRS) CC algorithm was (a) ported to NASA high end computing resources (HEC), (b) deprived of external dependencies, and (c) parallelized improving the processing by a factor of 70. The revised AIRS CC algorithm is now customizable, allowing user’s choice of channel selection, user’s model's fields as first guess, and could perform in real time. This study examines the benefits achieved when assimilating CCRs using the NASA’s Goddard Earth Observing System (GEOS) hybrid 4DEnVar system. The focus is on the 2017 Atlantic hurricane season with three infamous hurricanes (Harvey, Irma, and Maria) investigated in depth.  The impact of assimilating customized CCRs on the analyzed representation of tropical cyclone horizontal and vertical structure and on forecast skill is discussed.</p>