cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.48577/jpl.tdppci
title: Quantifying the tropical upper tropospheric warming amplification using radio occultation measurements

description: In the upper troposphere (UT), Global Navigation Satellite System (GNSS) Radio Occultations (ROs) provide accurate air temperatures (&lt;0.5 K) every ~200 m vertically. We use RO observations from 01/2002 until 12/2018 at 300, 250, and 200 hPa to quantify the tropical upper tropospheric amplification (defined as the ratio of temperature trends in the UT relative to the surface). We compare the RO-derived results against Atmospheric Infrared Sounder (AIRS), Coupled Model Intercomparison Project Phase 6 (CMIP6) Atmospheric Model Intercomparison Project (AMIP) models, Modern–Era Retrospective Analysis for Research and Applications version 2 (MERRA-2), and European Center for Medium-range Weather Forecasts Re-Analysis Interim (ERA-Interim) data. We find that CMIP6 AMIP models show excellent agreement with independent AIRS and RO observations on the magnitude of the UT warming and show warming that is significantly faster than both reanalyses above 250 hPa. Additionally, AIRS and CMIP6 AMIP present excellent agreement with the RO-measured tropical tropospheric amplification.
DOI: 10.48577/jpl.07vorw
title: Constraining the vertical distribution of coastal dust aerosol using OCO-2 O2 A-band measurements

description: Quantifying the vertical distribution of atmospheric aerosols is crucial for estimating their impact on the Earth’s energy budget and climate, improving forecast of air pollution in cities, and reducing biases in the retrieval of greenhouse gases (GHGs) from space. However, to date, passive remote sensing measurements have provided limited information about aerosol extinction profiles. In this study, we propose the use of a spectral sorting approach to constrain the aerosol vertical structure using spectra of reflected sunlight absorption within the molecular oxygen (O2) A-band collected by the Orbiting Carbon Observatory-2 (OCO-2). The effectiveness of the approach is evaluated using spectra acquired over the western Sahara coast by comparing the aerosol profile retrievals with lidar measurements from the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP). Using a radiative transfer model to simulate OCO-2 measurements, we found that high-resolution O2 A-band measurements have high sensitivity to aerosol optical depth (AOD) and aerosol layer height (ALH). Retrieved estimates of AOD and ALH based on a look up table technique show good agreement with CALIPSO measurements, with correlation coefficients of 0.65 and 0.53, respectively. The strength of the proposed spectral sorting technique lies in its ability to identify spectral channels with high sensitivity to AOD and ALH and extract the associated information from the observed radiance in a straightforward manner. The proposed approach has the potential to enable future passive remote sensing missions to map the aerosol vertical distribution on a global scale.
DOI: 10.48577/jpl.bk0jut
title: Application of Machine Learning to Hyperspectral Radiative Transfer Simulations

description: Hyperspectral observations have become one of the most popular and powerful methods for atmospheric remote sensing, and are widely used for temperature, gas, aerosol, and cloud retrievals. However, accurate forward radiative transfer simulations are computationally expensive since typical line-by-line approaches involve a larger number of monochromatic radiative transfer calculations. This study explores the feasibility of machine learning techniques for fast hyperspectral radiative transfer (HRT) simulations, which essentially performs calculations at a small fraction of hyperspectral wavelengths and extends them across the entire spectral range. A neural network (NN) model is used as an example for the development of the fast HRT, and its results are compared with those from a principal component analysis (PCA) model, which shares a similar principle. We consider hyperspectral radiances from both actual satellite observations and accurate line-by-line simulations. The NN model can alleviate the computational burden by two to three orders of magnitude, and generates radiances with small relative errors (generally less than 0.5% compared to exact calculations); the performance of the NN model is better than that of the PCA model. The model can be further improved by optimizing the training procedure and parameters, the representative wavelengths, and the machine learning technique itself.
DOI: 10.48577/jpl.d88glt
title: Cryogenic Temperature Induced Instability of 200MHz CMOS Operational Amplifier

description: This work investigates the electrical performance of a high speed general purpose operation amplifier, OPA2356, operating under cryogenic temperature -180oC. Evidence of cryogenic-induced instability of OPA2356 was experimentally observed and repeated using different hardware setups ─ a subtle increase in OPA2356’s Isupply current variations after extended -180oC cold dwell for 24 hours. The monitored Isupply current revealed increased random fluctuation of Isupply, characterized by its standard deviation σ, as compared to its initial room temperature current (σ25C-initial). This is exemplified by ~2σ 25C-initial at -180oC/24hrs and ~1.8σ 25C-initial at 25oC/post-cold. This work also suggests using the static supply current (Isupply), also commonly known as DC quiescent current, of the analog chip as a good monitor of analog chip’s instability operating under cryogenic conditions. This is demonstrated using another hardware setup where the OPA2356 was implemented as a unity-gain amplifier. We also found that increasing voltage headroom by maximizing the allowable VDD in analog chips will enable proper cryogenic operation of analog chips, which can be a critical trade-off for a space electronic system to consider between long term reliability and operating window.
DOI: 10.48577/jpl.jfla3d
title: The Benefits of Continuous Local Regression for Quantifying Global Warming

description: Change in global mean surface temperature (GMST), based on a blend of land air and ocean 20 water temperatures, is a widely cited climate change indicator that informs the Paris Agreement 21 goal to limit global warming since preindustrial to “well below” 2°C. Assessment of current 22 GMST enables determination of remaining target-consistent warming and therefore a relevant 23 remaining carbon budget. In recent IPCC reports, GMST was estimated via linear regression or 24 differences between decade-plus period means. We propose non-linear continuous local 25 regression (LOESS) using ±20 year windows to derive GMST across all periods of interest. 26 Using the three observational GMST datasets with almost complete interpolated spatial coverage 27 since the 1950s, we evaluate 1850—1900 to 2019 GMST as 1.14°C with a likely (17—83 %) 28 range of 1.05—1.25°C, based on combined statistical and observational uncertainty, compared 29 with linear regression of 1.05°C over 1880—2019. Performance tests in observational datasets 30 and two model large ensembles demonstrate that LOESS, like period mean differences, is 31 unbiased. However, LOESS also provides a statistical uncertainty estimate and gives warming 32 through 2019, rather than the 1850—1900 to 2010—2019 period mean difference centered at the 33 end of 2014. We derive historical global near-surface air temperature change (GSAT), using a 34 subset of CMIP6 climate models to estimate the adjustment required to account for the difference 35 between ocean water and ocean air temperatures. We find GSAT of 1.21°C (1.11—1.32°C) and 36 calculate remaining carbon budgets. We argue that continuous non-linear trend estimation offers 37 substantial advantages for assessment of long-term observational GMST.
DOI: 10.48577/jpl.n5ut6a
title: Assessment of CMIP6 Cloud Fraction and Comparison With Satellite Observations

description: The seasonal and regional variations of cloud fractions are compared across two generations of global climate model ensembles, specifically, the Coupled ModelIntercomparison Project-5 (CMIP5) and CMIP6, through the historical period in terms of skills and multi-model agreement. We find a wider spread of historical cloud fraction changes in the CMIP6 than was simulated by the previous CMIP5. The global mean cloud fractions of CMIP6 increased by about 4.5% from the CMIP5, which attributed to greater changes in the northernhemisphere (NH) than in the southern hemisphere (SH). The CMIP6 cloud fractions in recent years are validated with the CALIPSO_CLOUSAT observations to understand the cloud fraction uncertainties in CMIP6 models. The CMIP6 ensemble mean of cloud fractionscompares well with the observations with a mean difference of 0.5% in lower altitudes. The CMIP6 cloud fractions are higher than the observations at higher latitudes in both hemispheres in the upper troposphere, and the biases vary from one model to another. Thespatial difference between the ensemble and observations is further revealed over the tropics,where the model displays a 3% higher bias. In addition, we observed a significant trend occurring in the NH since the mid-20th century using calculations of cloud fraction trends based on the robust regression technique. Finally, we reduce the differences between the model and observations by applying a simple regression technique. The results exemplify that the model and modified observations compare well, with the root mean square value decreased bynearly 28% and the correlation increased significantly.
DOI: 10.48577/jpl.5ylvyi
title: Instrument Design and Performance of the High-Frequency Airborne Microwave and Millimeter-Wave Radiometer

description: The high-frequency airborne microwave and millimeter-wave radiometer (HAMMR) is a cross-track scanning airborne radiometer instrument with 25 channels from 18.7 to 183.3 GHz. HAMMR includes: low-frequency microwave channels at 18.7, 23.8, and 34.0 GHz at two linear-orthogonal polarizations; high-frequency millimeter-wave channels at 90, 130 and 168 GHz; and millimeter-wave sounding channels consisting of eight channels near the 118.75 GHz oxygen absorption line for temperature profiling and eight additional channels near the 183.31 GHz water vapor absorption line for water vapor profiling. HAMMR was deployed on a twin otter aircraft for a west coast flight campaign (WCFC) from November 4–17, 2014. During the WCFC, HAMMR collected radiometric observations for more than 53.5 h under diverse atmospheric conditions, including clear sky, scattered and dense clouds, as well as over a variety of surface types, including coastal ocean areas, inland water and land. These measurements provide a comprehensive dataset to validate the instrument.
DOI: 10.48577/jpl.lav1ms
title: ADVANCES IN PRECIPITATION ESTIMATION, PREDICTION AND IMPACT STUDIES

description: Precipitation continues to be one of the hardest variables to measure and predict at weather to climate scales. Research on space-time variability and prediction of precipitation lies at the intersection of atmospheric and hydrologic sciences and has important implications for society and the environment. The 12th International Precipitation Conference (IPC12), organized on June 19-21, 2019 in Irvine, California, followed on the footsteps of the previous successful IPC conferences and brought together the international community from more than 60 countries to discuss recent progress on precipitation research and contemplate the challenges and opportunities that lie ahead. IPC12 focused on three main themes: (1) global precipitation estimation from multiple sensors; (2) water cycle dynamics and predictive modeling at local to regional to global scales; and (3) hydrologic impacts of precipitation extremes and anticipated change. Given the challenges of climate variability and change, especially changes in precipitation extremes and seasonality including snowfall timing, efforts concentrated on recent advances in improved physical and statistical methodologies for sub-seasonal to seasonal (S2S) predictions using models and observations, global estimation of precipitation at high spatial and temporal resolution from multiple sensors, and assessment of uncertainty propagation to studies of floods, droughts and ecological changes. Pre-conference workshops and tutorials were offered on topics ranging from remote sensing estimation, to extreme value analysis to the National Water Model. One day of the IPC12 was dedicated to the “Soroosh Sorooshian Hydrometeorology Symposium” in honor of his extensive contributions to precipitation research over the past four decades and his unwavering commitment to producing improved remote sensing precipitation products for hydrologic applications worldwide. This is a brief report that integrates the conference outcomes and paves the path for future IPCs on the ever-important topic of understanding and quantifying precipitation variability and advancing measurement and prediction at a range of space and time scales relevant for decision support.
DOI: 10.48577/jpl.euxqf1
title: Wide discrepancies in the magnitude and direction of modeled solar-induced chlorophyll fluorescence in response to light conditions

description: Recent successes in passive remote sensing of far-red solar induced chlorophyll fluorescence (SIF) have spurred development and integration of canopy-level fluorescence models in global terrestrial biosphere models (TBMs) for climate and carbon cycle research. The interaction of fluorescence with photochemistry at the leaf- and canopy- scale provides opportunities to diagnose and constrain model simulations of photosynthesis and related processes, through direct comparison to and assimilation of tower, airborne, and satellite data. TBMs describe key processes relating to absorption of sunlight, leaf-level fluorescence emission, scattering and reabsorption throughout the canopy. Here, we analyze simulations from an ensemble of process-based TBM-SIF models (SiB3, SiB4, CLM4.5, CLM5.0, BETHY, ORCHIDEE, BEPS) at a subalpine evergreen needleleaf forest near Niwot Ridge, Colorado. These models are forced with tower observed meteorological data, and analyzed against continuous far-red SIF and gross primary productivity (GPP) partitioned eddy covariance data at diurnal and synoptic scales during the growing season (July-August 2017). Our primary objective is to summarize the site-level state of the art in TBM-SIF modeling over a relatively short time period (summer) when light, structure, and pigments are similar, setting the stage for regional- to global-scale analyses. We find that these models are generally well constrained in simulating photosynthetic yield, but show strongly divergent patterns in the simulation of absorbed photosynthetic active radiation (PAR), absolute GPP and fluorescence, quantum yields, and light response at leaf and canopy scale. This study highlights the need for mechanistic modeling of non-photochemical quenching in stressed and unstressed environments, and improved representation of light absorption (APAR), distribution of sunlit and shaded light, and radiative transfer from leaf to canopy scale.
DOI: 10.48577/jpl.afl4le
title: Evaluation of total electron content prediction using three ionosphere-thermosphere models

description: Prediction of ionospheric state is a critical space weather problem. We expand on our18 previous research of medium-range ionospheric forecasts and present new results on eval19uating prediction capabilities of three physics-based ionosphere-thermosphere models (TIE20GCM, CTIPe and GITM). The focus of our study is understanding how current mod21eling approaches may predict the global ionosphere for geomagnetic storms (as studied22 through 35 storms during 2000-2016). Prediction approach uses physics-based model23ing without any manual model adjustment, quality control or selection of the results. Our24 goal is to understand to what extent current physics-based modeling can be used in to25tal electron content (TEC) prediction and explore uncertainties of these prediction ef26forts with multi-day lead times. The ionosphere-thermosphere model runs are driven by27 actual interplanetary conditions, whether those data come from real-time measurements28 or predicted values themselves. These model runs were performed by the Community29 Coordinated Modeling Center (CCMC). JPL-produced global ionospheric maps (GIMs)30 were used to validate model TEC estimates. We utilize the True Skill Statistic (TSS)31 metric for the TEC prediction evaluation, noting that this is but one metric to assess32 predictive skill and that complete evaluations require combinations of such metrics. The33 meanings of contingency table elements for the prediction performance are analyzed in34 the context of ionosphere modeling. Prediction success is between about 0.2 and 0.5 for35 weak ionospheric disturbances, and decreases for strong disturbances. We evaluate the36 prediction of TEC decreases and increases. Our results indicate that physics-based mod37eling during storms shows promise in TEC prediction with multi-day lead time.
DOI: 10.48577/jpl.dexhxn
title: A look back, part II: The drilling campaign of the Curiosity rover during the Mars Science Laboratory's second and third Martian years

description: The Mars Science Laboratory (MSL) rover, Curiosity, completed its second Martian year, 1337 sols (1374 Earth days), of operation on May 11, 2016, and its third Martian year, 2006 sols (2061 Earth days), of operation on March 28, 2018. During this time the rover successfully drilled twelve full depth drill holes into the Martian surface and analyzed the recovered material using onboard instruments, giving us new insights into the potential habitability and geologic diversity of ancient Mars. During the second Martian year, four holes were sunk into the mudstones of the Murray formation: 'Confidence Hills' (Sol 759), 'Mojave 2' (Sol 882), 'Telegraph Peak' (908) & 'Buckskin' (Sol 1060); while four more holes were sunk into the sandstones of the Stimson formation: 'Big Sky' (Sol 1119), 'Greenhorn' (Sol 1137), 'Lubango' (Sol 1320) & 'Okoruso' (Sol 1332). During the third Martian year, four additional holes were sunk into the Murray formation: 'Oudam' (Sol 1361), 'Marimba' (Sol 1422), 'Quela' (Sol 1464) & 'Sebina' (Sol 1495). In this paper, we will give a brief overview of the rover sampling hardware and nominal drilling protocols, followed by a discussion of how these protocols were refined and altered early during the course of Curiosity's second year on Mars. In addition, we will describe the 'Bonanza King' (Sol 724) drill campaign, the mission's first 'successful failure', and how it influenced these changes. We will also briefly discuss the events leading up to the drill feed fault on Sol 1536, which resulted in suspension of all drill activities for the remainder of the third Martian year. Finally, we will present scientific highlights obtained from each drill site utilizing MSL's onboard instrumentation (SAM & CheMin), results enabled by the drill’s ability to excavate sample at depth and transfer it to these instruments.
DOI: 10.48577/jpl.euwqcl
title: Windows of Opportunity for Skillful Forecasts Subseasonal to Seasonal and Beyond

description: There is high demand and a growing expectation for predictions of environmental conditions that go beyond 0-14 day weather forecasts with outlooks extending to one or more seasons and beyond. This is driven by the needs of the energy, water management, and agriculture sectors, to name a few. There is an increasing realization that, unlike weather forecasts, prediction skill on longer timescales can leverage specific climate phenomena or conditions for a predictable signal above the weather noise. Currently, it is understood that these conditions are intermittent in time and have spatially heterogeneous impacts on skill, hence providing strategic windows of opportunity for skillful forecasts. Research points to such windows of opportunity, including El Niño or La Niña events, active periods of the Madden-Julian Oscillation, disruptions of the stratospheric polar vortex, when certain large-scale atmospheric regimes are in place, or when persistent anomalies occur in the ocean or land surface. Gains could be obtained by increasingly developing prediction tools and metrics that strategically target these specific windows of opportunity. Across the globe, re-evaluating forecasts in this manner could find value in forecasts previously discarded as not skillful. Users’ expectations for prediction skill could be more adequately met, as they are better aware of when and where to expect skill and if the prediction is actionable. Given that there is still untapped potential, in terms of process understanding and prediction methodologies, it is safe to expect that in the future forecast opportunities will expand. Process research and the development of innovative methodologies will aid such progress.
DOI: 10.48577/jpl.kvdy9a
title: Joint Analysis of Convective Structure from the APR-2 Precipitation Radar and the DAWN Doppler Wind Lidar During the 2017 Convective Processes Experiment (CPEX)

description: The mechanisms linking convection and cloud dynamical processes is a major factor in much of the uncertainty in both weather and climate prediction. Further constraining the uncertainty in convective cloud processes linking 3-D air motion and cloud structure through models and observations is vital for improvements in weather forecasting, and understanding limits on atmospheric predictability. To date, there have been relatively few airborne observations specifically targeted for sampling convective cloud processes linking 3-D air motion and transport of water vapor near clouds, and the subsequent development (or non-development) of convective precipitation. During the May-June 2017 Convective Processes Experiment (CPEX), NASA DC-8-based airborne observations were collected from the JPL Ku/Ka-band Airborne Precipitation Radar (APR-2) and the 2-um Doppler Aerosol Wind (DAWN) lidar during approximately 100 flight hours. Frequent dropsonde data accompanied the DAWN observations for validation purposes, and to provide complement wind profiles in and near convection. For CPEX, the APR-2 provided vertical air motion and structure of the cloud systems in nearby precipitating regions where DAWN is unable to sense. Conversely, DAWN sampled vertical wind profiles in aerosol-rich regions surrounding the convection, but is unable to sense the wind field structure within most clouds. In this manuscript, the complementary nature of these data are presented from the June 10-11 flight dates, including the APR-2 precipitation structure and Doppler wind fields, and adjacent wind profiles from the DAWN and dropsonde data.
DOI: 10.48577/jpl.87rkwi
title: Nowcasting Earthquakes in Southern California with Machine Learning: Bursts, Swarms and Aftershocks May Be Related to Levels of Regional Tectonic Stress

description: Earthquake nowcasting is a method to evaluate the current state of seismic hazard from large earthquakes. In this paper, we connect the temporal occurrence of the largest and most potentially destructive earthquakes in California since 1984 with a readily observable property of small earthquake seismicity in the region. Our method involves the calculation of the time history of the average radius (horizontal size or extent) of "bursts" of small earthquakes, in the time leading up to and following major earthquakes in the region. We observe that the radius systematically and gradually decreases leading up to major earthquakes, increasing suddenly and discontinuously following the event. This observable pattern resembles the long-hypothesized cycle of regional tectonic stress buildup and release, or elastic rebound, associated with large destructive earthquakes. We propose that the radius of these bursts might be considered to be a proxy variable for the changing state of regional stress in Southern California.