Science through Machine Learning: Quantification of Poststorm Thermospheric Cooling

Machine learning (ML) is often viewed as a black-box regression technique that is unable to provide considerable scientific insight. ML models are universal function approximators and - if used correctly - can provide scientific information related to the ground-truth dataset used for fitting. A benefit to ML over parametric models is that there are no predefined basis functions limiting the phenomena that can be modeled. In this work, we develop ML models on three datasets: the Space Environment Technologies (SET) High Accuracy Satellite Drag Model (HASDM) density database, a spatiotemporally matched dataset of outputs from the Jacchia-Bowman 2008 Empirical Thermospheric Density Model (JB2008), and an accelerometer-derived density dataset from CHAllenging Minisatellite Payload (CHAMP). These ML models are compared to the Naval Research Laboratory Mass Spectrometer and Incoherent Scatter radar (NRLMSIS 2.0) model to study the presence of post-storm cooling in the middle-thermosphere. We find that both NRLMSIS 2.0 and JB2008-ML do not account for post-storm cooling and consequently perform poorly in periods following strong geomagnetic storms (e.g. the 2003 Halloween storms). Conversely, HASDM-ML and CHAMP-ML do show evidence of post-storm cooling indicating that this phenomenon is present in the original datasets. Results show that density reductions up to 40% can occur 1--3 days post-storm depending on location and the strength of the storm

Linkages between the cold summer mesopause and thermospheric zonal mean circulation

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/95392/1/grl28827.pd

NRLMSIS 2.1: An Empirical Model of Nitric Oxide Incorporated Into MSIS

We have developed an empirical model of nitric oxide (NO) number density at altitudes from similar to 73 km to the exobase, as a function of altitude, latitude, day of year, solar zenith angle, solar activity, and geomagnetic activity. The model is part of the NRLMSIS (R) 2.1 empirical model of atmospheric temperature and species densities; this upgrade to NRLMSIS 2.0 consists solely of the addition of NO. MSIS 2.1 assimilates observations from six space-based instruments: UARS/HALOE, SNOE, Envisat/MIPAS, ACE/FTS, Odin/SMR, and AIM/SOFIE. We additionally evaluated the new model against independent extant NO data sets. In this paper, we describe the formulation and fitting of the model, examine biases between the data sets and model and among the data sets, compare with another empirical NO model (NOEM), and discuss scientific aspects of our analysis

NRLMSIS 2.1: An Empirical Model of Nitric Oxide Incorporated Into MSIS

We have developed an empirical model of nitric oxide (NO) number density at altitudes from ∼73 km to the exobase, as a function of altitude, latitude, day of year, solar zenith angle, solar activity, and geomagnetic activity. The model is part of the NRLMSIS® 2.1 empirical model of atmospheric temperature and species densities; this upgrade to NRLMSIS 2.0 consists solely of the addition of NO. MSIS 2.1 assimilates observations from six space-based instruments: UARS/HALOE, SNOE, Envisat/MIPAS, ACE/FTS, Odin/SMR, and AIM/SOFIE. We additionally evaluated the new model against independent extant NO data sets. In this paper, we describe the formulation and fitting of the model, examine biases between the data sets and model and among the data sets, compare with another empirical NO model (NOEM), and discuss scientific aspects of our analysis

Comparison of co-located independent ground-based middle atmospheric wind and temperature measurements with numerical weather prediction models

High-resolution, ground-based and independent observations including co-located windradiometer, lidar stations, and infrasound instruments are used to evaluate the accuracy of general circulationmodels and data-constrained assimilation systems in the middle atmosphere at northern hemispheremidlatitudes. Systematic comparisons between observations, the European Centre for Medium-Range WeatherForecasts (ECMWF) operational analyses including the recent Integrated Forecast System cycles 38r1 and 38r2,the NASA’s Modern-Era Retrospective Analysis for Research and Applications (MERRA) reanalyses, and thefree-running climate Max Planck Institute–Earth System Model–Low Resolution (MPI-ESM-LR) are carried out inboth temporal and spectral dom ains. We find that ECMWF and MERRA are broadly consistent with lidar and windradiometer measurements up to ~40 km. For both temperature and horizontal wind components, deviationsincrease with altitude as the assimilated observations become sparser. Between 40 and 60 km altitude, thestandard deviation of the mean difference exceeds 5 K for the temperature and 20 m/s for the zonal wind. Thelargest deviations are observed in winter when the variability from large-scale planetary waves dominates.Between lidar data and MPI-ESM-LR, there is an overall agreement in spectral amplitude down to 15–20 days. Atshorter time scales, the variability is lacking in the model by ~10 dB. Infrasound observations indicate a generalgood agreement with ECWMF wind and temperature products. As such, this study demonstrates the potentialof the infrastructure of the Atmospheric Dynamics Research Infrastructure in Europe project that integratesvarious measurements and provides a quantitative understanding of stratosphere-troposphere dynamicalcoupling for numerical weather prediction applications

NRLMSIS 2.0: A Whole-Atmosphere Empirical Model of Temperature and Neutral Species Densities

NRLMSIS® 2.0 is an empirical atmospheric model that extends from the ground to the exobase and describes the average observed behavior of temperature, eight species densities, and mass density via a parametric analytic formulation. The model inputs are location, day of year, time of day, solar activity, and geomagnetic activity. NRLMSIS 2.0 is a major, reformulated upgrade of the previous version, NRLMSISE-00. The model now couples thermospheric species densities to the entire column, via an effective mass profile that transitions each species from the fully mixed region below ~70 km altitude to the diffusively separated region above ~200 km. Other changes include the extension of atomic oxygen down to 50 km and the use of geopotential height as the internal vertical coordinate. We assimilated extensive new lower and middle atmosphere temperature, O, and H data, along with global average thermospheric mass density derived from satellite orbits, and we validated the model against independent samples of these data. In the mesosphere and below, residual biases and standard deviations are considerably lower than NRLMSISE-00. The new model is warmer in the upper troposphere and cooler in the stratosphere and mesosphere. In the thermosphere, N2 and O densities are lower in NRLMSIS 2.0; otherwise, the NRLMSISE-00 thermosphere is largely retained. Future advances in thermospheric specification will likely require new in situ mass spectrometer measurements, new techniques for species density measurement between 100 and 200 km, and the reconciliation of systematic biases among thermospheric temperature and composition data sets, including biases attributable to long-term changes

NRLMSIS 2.0: A Whole-Atmosphere Empirical Model of Temperature and Neutral Species Densities

NRLMSIS® 2.0 is an empirical atmospheric model that extends from the ground to the exobase and describes the average observed behavior of temperature, eight species densities, and mass density via a parametric analytic formulation. The model inputs are location, day of year, time of day, solar activity, and geomagnetic activity. NRLMSIS 2.0 is a major, reformulated upgrade of the previous version, NRLMSISE-00. The model now couples thermospheric species densities to the entire column, via an effective mass profile that transitions each species from the fully mixed region below ~70 km altitude to the diffusively separated region above ~200 km. Other changes include the extension of atomic oxygen down to 50 km and the use of geopotential height as the internal vertical coordinate. We assimilated extensive new lower and middle atmosphere temperature, O, and H data, along with global average thermospheric mass density derived from satellite orbits, and we validated the model against independent samples of these data. In the mesosphere and below, residual biases and standard deviations are considerably lower than NRLMSISE-00. The new model is warmer in the upper troposphere and cooler in the stratosphere and mesosphere. In the thermosphere, N2 and O densities are lower in NRLMSIS 2.0; otherwise, the NRLMSISE-00 thermosphere is largely retained. Future advances in thermospheric specification will likely require new in situ mass spectrometer measurements, new techniques for species density measurement between 100 and 200 km, and the reconciliation of systematic biases among thermospheric temperature and composition data sets, including biases attributable to long-term changes

HL‐TWiM Empirical Model of High‐Latitude Upper Thermospheric Winds

We present an empirical model of thermospheric winds (High‐latitude Thermospheric Wind Model [HL‐TWiM]) that specifies F region high‐latitude horizontal neutral winds as a function of day of year, latitude, longitude, local time, and geomagnetic activity. HL‐TWiM represents the large‐scale neutral wind circulation, in geomagnetic coordinates, for the given input conditions. The model synthesizes the most extensive collection to date of historical high‐latitude wind measurements; it is based on statistical analyses of several decades of F region thermospheric wind measurements from 21 ground‐based stations (Fabry‐Perot Interferometers and Scanning Doppler Imaging Fabry‐Perot Interferometers) located at various northern and southern high latitudes and two space‐based instruments (UARS WINDII and GOCE). The geomagnetic latitude and local time dependences in HL‐TWiM are represented using vector spherical harmonics, day of year and longitude variations are represented using simple harmonic functions, and the geomagnetic activity dependence is represented using quadratic B splines. In this paper, we describe the HL‐TWiM formulation and fitting procedures, and we verify the model against the neutral wind databases used in its formulation. HL‐TWiM provides a necessary benchmark for validating new wind observations and tuning our physical understanding of complex wind behaviors. Results show stronger Universal Time variation in winds at southern than northern high latitudes. Model‐data intra‐annual comparisons in this study show semiannual oscillation‐like behavior of GOCE winds, rarely observed before in wind data.Key PointsWe developed a comprehensive empirical model of high‐latitude F region thermospheric winds (HL‐TWiM)Universal Time variations in high‐latitude winds are stronger in the Southern than Northern HemisphereHL‐TWiM provides a necessary benchmark for validating new high‐latitude wind observations and tuning first principal modelsPeer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/153588/1/jgra55363_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/153588/2/jgra55363-sup-0001-Figure_SI-S01.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/153588/3/jgra55363.pd