Infrared Satellite Observations Research Articles

Error Model for the Assimilation of All-Sky FY-4A/AGRI Infrared Radiance Observations.

The Advanced Geostationary Radiation Imager (AGRI) carried by the FengYun-4A (FY-4A) satellite enables the continuous observation of local weather. However, FY-4A/AGRI infrared satellite observations are strongly influenced by clouds, which complicates their use in all-sky data assimilation. The presence of clouds leads to increased uncertainty, and the observation-minus-background (O-B) differences can significantly deviate from the Gaussian distribution assumed in the variational data assimilation theory. In this study, we introduce two cloud-affected (Ca) indices to quantify the impact of cloud amount and establish dynamic observation error models to address biases between O-B and Gaussian distributions when assimilating all-sky data from FY-4A/AGRI observations. For each Ca index, we evaluate two dynamic observation error models: a two-segment and a three-segment linear model. Our findings indicate that the three-segment linear model we propose better conforms to the statistical characteristics of FY-4A/AGRI observations and improves the Gaussianity of the O-B probability density function. Dynamic observation error models developed in this study are capable of handling cloud-free or cloud-affected FY-4A/AGRI observations in a uniform manner without cloud detection.

Spatio-temporal variability of small-scale leads based on helicopter maps of winter sea ice surface temperatures

Observations of sea ice surface temperature provide crucial information for studying Arctic climate, particularly during winter. We examined 1 m resolution surface temperature maps from 35 helicopter flights between October 2, 2019, and April 23, 2020, recorded during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC). The seasonal cycle of the average surface temperature spanned from 265.6 K on October 2, 2019, to 231.8 K on January 28, 2020. The surface temperature was affected by atmospheric changes and varied across scales. Leads in sea ice (cracks of open water) were of particular interest because they allow greater heat exchange between ocean and atmosphere than thick, snow-covered ice. Leads were classified by a temperature threshold. The lead area fraction varied between 0% and 4% with higher variability on the local (5–10 km) than regional scale (20–40 km). On the regional scale, it remained stable at 0–1% until mid-January, increasing afterward to 4%. Variability in the lead area is caused by sea ice dynamics (opening and closing of leads), as well as thermodynamics with ice growth (lead closing). We identified lead orientation distributions, which varied between different flights but mostly showed one prominent orientation peak. The lead width distribution followed a power law with a negative exponent of 2.63, which is in the range of exponents identified in other studies, demonstrating the comparability to other data sets and extending the existing power law relationship to smaller scales down to 3 m. The appearance of many more narrow leads than wide leads is important, as narrow leads are not resolved by current thermal infrared satellite observations. Such small-scale lead statistics are essential for Arctic climate investigations because the ocean–atmosphere heat exchange does not scale linearly with lead width and is larger for narrower leads.

PyRTlib: an educational Python-based library for non-scattering atmospheric microwave radiative transfer computations

Abstract. This article introduces PyRTlib, a new standalone Python package for non-scattering line-by-line microwave radiative transfer simulations. PyRTlib is a flexible and user-friendly tool for computing down- and upwelling brightness temperatures and related quantities (e.g., atmospheric absorption, optical depth, opacity, mean radiating temperature) written in Python, a language commonly used nowadays for scientific software development, especially by students and early-career scientists. PyRTlib allows for simulating observations from ground-based, airborne, and satellite microwave sensors in clear-sky and in cloudy conditions (under non-scattering Rayleigh approximation). The intention for PyRTlib is not to be a competitor to state-of-the-art atmospheric radiative transfer codes that excel in speed and/or versatility (e.g., ARTS, Atmospheric Radiative Transfer Simulator; RTTOV, Radiative Transfer for TOVS (Television Infrared Observation Satellite (TIROS) Operational Vertical Sounder)). The intention is to provide an educational tool, completely written in Python, to readily simulate atmospheric microwave radiative transfer from a variety of input profiles, including predefined climatologies, global radiosonde archives, and model reanalysis. The paper presents quick examples for the built-in modules to access popular open data archives. The paper also presents examples for computing the simulated brightness temperature for different platforms (ground-based, airborne, and satellite), using various input profiles, showing how to easily modify other relevant parameters, such as the observing angle (zenith, nadir, slant), surface emissivity, and gas absorption model. PyRTlib can be easily embedded in other Python codes needing atmospheric microwave radiative transfer (e.g., surface emissivity models and retrievals). Despite its simplicity, PyRTlib can be readily used to produce present-day scientific results, as demonstrated by two examples showing (i) an absorption model comparison and validation with ground-based radiometric observations and (ii) uncertainty propagation of spectroscopic parameters through the radiative transfer calculations following a rigorous approach. To our knowledge, the uncertainty estimate is not provided by any other currently available microwave radiative transfer code, making PyRTlib unique for this aspect in the atmospheric microwave radiative transfer code scenario.

Potential impact of all‐sky assimilation of visible and infrared satellite observations compared with radar reflectivity for convective‐scale numerical weather prediction

AbstractAlthough cloud‐affected satellite observations are heavily used for nowcasting applications, their use in regional data assimilation is very limited despite possible benefits for convective‐scale forecasts. In this article, we estimate the potential impact of assimilating cloud‐affected satellite observations of visible (0.6 m) and near thermal infrared wavelengths (6.2 and 7.3 m) relative to the impact of assimilating radar reflectivity observations. We employed observing‐system simulation experiments with a perfect‐model forecast for two cases of strong convective summertime precipitation. Observations are simulated using the radiative transfer model RTTOV/MFASIS and assimilated by the ensemble adjustment Kalman filter in the Data Assimilation Research Testbed. The Weather Research and Forecasting model at 2‐km grid resolution was used for forecasts. Results show that satellite observations can be nearly as beneficial as three‐dimensional radar reflectivity observations. Under favorable conditions, where the prior contains no error in the stage of storm development but only in horizontal position and strength, the assimilation of visible observations leads to 88% of the radar impact. Under more difficult conditions, the impact of visible and infrared observations still reached 50 and 79%, respectively.

Potential impact of assimilating visible and infrared satellite observations compared to radar reflectivity for convective‐scale NWP

Potential impact of assimilating visible and infrared satellite observations compared to radar reflectivity for convective‐scale NWP

Assessing Sea Surface Temperatures Estimated from Fused Infrared and Microwave Data

Sea surface temperature (SST), a critical parameter of the global ocean–atmosphere system, is an essential element in the study and in the application of marine science. Satellite–infrared observations currently represent the only available method for continuous, large-scale observation of SST. Although passive microwave observations are not blocked by clouds, allowing for data collection in all weather conditions, this technological tool is characterized by low spatial resolution. Conversely, infrared observations offer high resolution but are susceptible to cloud obscuration. Accordingly, a technique that effectively fuses microwave and infrared satellite observations into a high-resolution SST field with global coverage close to the actual distribution is of practical significance. This paper describes fusing MODIS infrared remote sensing and AMSR-2 microwave remote sensing SST data with an optimal interpolation (OI) approach to produce a high-resolution SST data. The study chose the coastal Kuroshio region of China to establish an appropriate scale for examining the spatial structure of SST and attaining a more realistic picture of SST observations and impacts. The included discussion of the sources of error in the fusion process provides a reference for improving the accuracy of fused marine remote sensing data. The study also compared the fused SST results and the current international mainstream multi-temporal resolution of the three using the OI algorithm. We compared the fusion product with ARGO data with and without typhoon impact to explore and practice the OI in SST fusion when evaluating the accuracy of different data in the case of external disturbance being present. The research results have great significance for improving regional SST forecast accuracy while ensuring the applicability of various approaches to fusing SST data by incorporating the influence of typhoons in the offshore region of the East China Sea (ECS). Implications for the future development of SST fusion data are also included in the discussion.

The IASI Water Deficit Index to Monitor Vegetation Stress and Early Drying in Summer Heatwaves: An Application to Southern Italy

The boreal hemisphere has been experiencing increasing extreme hot and dry conditions over the past few decades, consistent with anthropogenic climate change. The continental extension of this phenomenon calls for tools and techniques capable of monitoring the global to regional scales. In this context, satellite data can satisfy the need for global coverage. The main objective we have addressed in the present paper is the capability of infrared satellite observations to monitor the vegetation stress due to increasing drought and heatwaves in summer. We have designed and implemented a new water deficit index (wdi) that exploits satellite observations in the infrared to retrieve humidity, air temperature, and surface temperature simultaneously. These three parameters are combined to provide the water deficit index. The index has been developed based on the Infrared Atmospheric Sounder Interferometer or IASI, which covers the infrared spectral range 645 to 2760 cm−1 with a sampling of 0.25 cm−1. The index has been used to study the 2017 heatwave, which hit continental Europe from May to October. In particular, we have examined southern Italy, where Mediterranean forests suffer from climate change. We have computed the index’s time series and show that it can be used to indicate the atmospheric background conditions associated with meteorological drought. We have also found a good agreement with soil moisture, which suggests that the persistence of an anomalously high water deficit index was an essential driver of the rapid development and evolution of the exceptionally severe 2017 droughts.

Deep Convection as Inferred From the C2OMODO Concept of a Tandem of Microwave Radiometers

Probing the atmosphere from space using radiometers is a challenging way to improve our knowledge of deep convection. Exploration of water absorption bands in the 183, 325, and 448 GHz range is promising because of the radiation scattered by icy hydrometeors produced by deep convection. We investigate what properties of deep convection could be inferred from the Convective Core Observations through MicrOwave Derivatives in the trOpics (C2OMODO) concept of a tandem of microwave radiometers separated by several tens of seconds. Two tropical deep convective events (Hector the Convector and a radiative-convective equilibrium case) are simulated with the Meso-NH non-hydrostatic numerical model, the outputs of which are used to compute brightness temperatures (Tbs) using the Radiative Transfer for the Television and Infrared Observation Satellite (TIROS) Operational Vertical Sounder (RTTOV) code. We find different relationships between the ice water path, vertical ice momentum, vertical ice velocity, and the time derivative dTb/dt. They depend on where they are probed after separating the growing convective cores from their surrounding environment with a radiometric point of view. Tb and dTb/dt are highly dependent on the ice water path that depends on horizontal and vertical ice advection and microphysical processes. Looking at deep convection in general, we find that the ice water path increases linearly with decreasing dTb/dt. In the specific case of the core of growing convective cells, the vertical ice momentum and the vertical ice velocity are related to dTb/dt. However, such a relationship breaks down in the anvil because horizontal ice advection can dominate microphysical processes. These results are robust to horizontal resolution and time delay.

Nocturnal survival of isoprene linked to formation of upper tropospheric organic aerosol.

Isoprene is emitted mainly by terrestrial vegetation and is the dominant volatile organic compound (VOC) in Earth's atmosphere. It plays key roles in determining the oxidizing capacity of the troposphere and the formation of organic aerosol. Daytime infrared satellite observations of isoprene reported here broadly agree with emission inventories, but we found substantial differences in the locations and magnitudes of isoprene hotspots, consistent with a recent study. The corresponding nighttime infrared observations reveal unexpected hotspots over tropical South America, the Congo basin, and Southeast Asia. We used an atmospheric chemistry model to link these nighttime isoprene measurements to low-NOx regions with high biogenic VOC emissions; at sunrise the remaining isoprene can lead to the production of epoxydiols and subsequently to the widespread seasonal production of organic aerosol in the tropical upper troposphere.

Data Fusion of GEO FY-4A GIIRS and LEO Hyperspectral Infrared Sounders with Surface Observations: A Hong Kong Case Study

Abstract Hyperspectral infrared satellite observations from geostationary platforms allow for the retrieval of temperature and water vapor measurements with higher temporal and vertical resolution than was previously available. The Chinese satellite Fengyun-4A (FY-4A) includes the Geostationary Interferometric Infrared Sounder (GIIRS), which has the ability to measure vertical profiles of temperature and water vapor from space at times when ground-based upper-air soundings are not available and can fill an important need in short-range weather prediction. In this study, convective available potential energy (CAPE) and lifted index (LI), which are used for forecasting atmospheric instability, were computed using the SHARPpy algorithm used by the NWS Storm Prediction Center. However, remote infrared and microwave sensing is lacking detailed information in the boundary layer, so the addition of the NOAA Meteorological Assimilation and Data Ingest System (MADIS) surface data may be necessary in order to get accurate temperature and moisture measurement near the surface. This study uses 10–16 May 2019 in the coastal region near Hong Kong for evaluating the use of hourly surface observations combined with satellite soundings from FY-4A GIIRS at 2-h intervals. The GIIRS plus MADIS surface-based CAPE and LI estimates are compared to estimates derived from low-Earth-orbiting (LEO) SNPP and NOAA-20 from NOAA, MetOp from EUMETSAT, NWP reanalysis, and local radiosondes. In the case study, the 2-h sampling interval of the GIIRS geostationary sounder was able to capture the rapid transition (16 h) from a stable to an unstable atmosphere in both CAPE and LI. The use of surface observations with satellite soundings gave mixed results, possibly due to the complex terrain near Hong Kong.

A Daytime Multisensor Satellite System for Global Gas Flaring Monitoring

Natural gas flaring (GF) is a longstanding problem for the oil industry. Recent estimates indicate that this phenomenon has increased to levels recorded a full decade earlier. While in 2020 there was a decline in global GF due to COVID-19 pandemic, data suggest that GF continues to be a persistent issue, with solutions remaining difficult or uneconomical in certain countries. Nighttime satellite products are widely used to map and monitor GF affected areas, partially filling the general lack of information from oil companies and/or national reporting. In this work, we assess the potential of daytime infrared satellite observations at high spatial resolution from operational land imager (OLI) and multispectral instrument (MSI) sensors, respectively, onboard Landsat-8 (L8) and Sentinel-2 (S2) satellites, in monitoring GF activity. The normalized hotspot indices (NHI) algorithm is used for this purpose, testing its performance over six different GF sites. Results show the NHI capability in providing accurate information about GF-related thermal (e.g., 100% of detections offshore; up to 92% onshore), despite some limitations due to clouds and smoke. They open challenging scenarios regarding the possibility of quantifying the volume of emitted gas from daytime S2-MSI and L8-OLI data, integrating information from well-established nighttime operational systems.

УСВОЕНИЕ ДАННЫХ СПУТНИКОВЫХ МЕТЕОРОЛОГИЧЕСКИХ НАБЛЮДЕНИЙ В ГИДРОМЕТЦЕНТРЕ РОССИИ

Results of studies on analysis, correction, and assimilation of microwave and infrared satellite observations in the global data assimilation system of the Hydrometcenter of Russia are presented. Technique and results of adaptive correction (external calibration) of atmospheric sounding and imaging channels of the microwave radiometer MTVZA-GYa on board the Russian polar orbiting satellites of Meteor-M series are described. Assimilation of observations from the hyperspectral infrared Fourier spectrometer IFKS-2, which is also a part of the Meteor-M satellites payload, is presented. The assimilation technique includes a new scheme for selection of the most informative channels through orthogonalization of the so called Jacobians. Numerical experiments show that short-range forecasts almost equally benefit from assimilation of IKFS-2 and IASI data.

Weatherscapes

Due to the complex interplay of various meteorological phenomena, simulating weather is a challenging and open research problem. In this contribution, we propose a novel physics-based model that enables simulating weather at interactive rates. By considering atmosphere and pedosphere we can define the hydrologic cycle - and consequently weather - in unprecedented detail. Specifically, our model captures different warm and cold clouds, such as mammatus, hole-punch, multi-layer, and cumulonimbus clouds as well as their dynamic transitions. We also model different precipitation types, such as rain, snow, and graupel by introducing a comprehensive microphysics scheme. The Wegener-Bergeron-Findeisen process is incorporated into our Kessler-type microphysics formulation covering ice crystal growth occurring in mixed-phase clouds. Moreover, we model the water run-off from the ground surface, the infiltration into the soil, and its subsequent evaporation back to the atmosphere. We account for daily temperature changes, as well as heat transfer between pedosphere and atmosphere leading to a complex feedback loop. Our framework enables us to interactively explore various complex weather phenomena. Our results are assessed visually and validated by simulating weatherscapes for various setups covering different precipitation events and environments, by showcasing the hydrologic cycle, and by reproducing common effects such as Foehn winds. We also provide quantitative evaluations creating high-precipitation cumulonimbus clouds by prescribing atmospheric conditions based on infrared satellite observations. With our model we can generate dynamic 3D scenes of weatherscapes with high visual fidelity and even nowcast real weather conditions as simulations by streaming weather data into our framework.

Understanding the model representation of clouds based on visible and infrared satellite observations

Abstract. There is a rising interest in improving the representation of clouds in numerical weather prediction models. This will directly lead to improved radiation forecasts and, thus, to better predictions of the increasingly important production of photovoltaic power. Moreover, a more accurate representation of clouds is crucial for assimilating cloud-affected observations, in particular high-resolution observations from instruments on geostationary satellites. These observations can also be used to diagnose systematic errors in the model clouds, which are influenced by multiple parameterisations with many, often not well-constrained, parameters. In this study, the benefits of using both visible and infrared satellite channels for this purpose are demonstrated. We focus on visible and infrared Meteosat SEVIRI (Spinning Enhanced Visible InfraRed Imager) images and their model equivalents computed from the output of the ICON-D2 (ICOsahedral Non-hydrostatic, development version based on version 2.6.1; Zängl et al., 2015) convection-permitting, limited area numerical weather prediction model using efficient forward operators. We analyse systematic deviations between observed and synthetic satellite images derived from semi-free hindcast simulations for a 30 d summer period with strong convection. Both visible and infrared satellite observations reveal significant deviations between the observations and model equivalents. The combination of infrared brightness temperature and visible reflectance facilitates the attribution of individual deviations to specific model shortcomings. Furthermore, we investigate the sensitivity of model-derived visible and infrared observation equivalents to modified model and visible forward operator settings to identify dominant error sources. Estimates of the uncertainty of the visible forward operator turned out to be sufficiently low; thus, it can be used to assess the impact of model modifications. Results obtained for various changes in the model settings reveal that model assumptions on subgrid-scale water clouds are the primary source of systematic deviations in the visible satellite images. Visible observations are, therefore, well-suited to constrain subgrid cloud settings. In contrast, infrared channels are much less sensitive to the subgrid clouds, but they can provide information on errors in the cloud-top height.

Improving the Analyses and Forecasts of a Tropical Squall Line Using Upper Tropospheric Infrared Satellite Observations

Improving the Analyses and Forecasts of a Tropical Squall Line Using Upper Tropospheric Infrared Satellite Observations

Reconstruction of remotely sensed daily evapotranspiration data in cloudy-sky conditions

The unavailability of thermal infrared satellite observations in cloudy conditions has limited the spatial distribution and temporal coverage of remotely sensed evapotranspiration (ET) data. As a result, a number of approaches have been developed to reconstruct remotely sensed daily ET data in cloudy-sky conditions. Despite the wide application of these approaches, no work has been conducted to compare their performance over a wide variety of climatic and vegetative conditions. In this study, three commonly used ET reconstruction approaches namely, reference ET fraction (EToF), land surface temperature reconstruction (LSTR), and variational data assimilation (VDA) are used to obtain daily ET data under cloudy conditions. The abovementioned three approaches are applied to the Heihe River Basin (HRB) in the northwest of China during the growing season in 2015. The HRB covers an area of approximately 1,432,000 km2 and contains various land covers. The large aperture scintillometer (LAS) and eddy covariance (EC) measurements are used to evaluate performance of the abovementioned three methods over the grassland, cropland, and riparian forest sites in the HRB. The results show that the EToF approach underestimates ET at the grassland and riparian sites in cloudy days because of the negatively biased ET/ETo (where ETo is the reference evapotranspiration) in clear-sky days. The VDA approach overestimates ET in cloudy-sky conditions because of the overpredicted evaporative fraction values. The LSTR approach overestimates ET due to the under-reconstructed LST in cloudy day. The mean absolute percentage difference (MAPD) and root mean square error (RMSE) statistical metrics are used to compare performance of the three approaches The LSTR (MAPD = 43.1% and RMSE = 1.3 mm/day) and VDA (MAPD = 46.7% and RMSE = 1.4 mm/day) approaches slightly outperform EToF (MAPD = 46.6% and RMSE = 1.5 mm/day). The RMSEs of reconstructed ET values by EToF, VDA, and LSTR increase respectively from 1.6, 1.2, and 1.5 (mm/day) to 2.2, 1.9, and 2.0 (mm/day) as the number of consecutive cloudy days increases from 1 to 3. These outcomes suggest that synergistic use of space-borne microwave and thermal infrared LST observations into remote sensing-based ET methods can improve the reconstruction of ET data under cloudy conditions.

Large Mesoscale Eddies in the Western Arctic Ocean From Satellite Altimetry Measurements

AbstractEddies in the Western Arctic play an important role in transporting heat, salt, and biogeochemical tracers across the continental shelf and within the deep basins. However, comprehensive observations of the regional and temporal variability of the eddy field characteristics are difficult to obtain remotely due to the presence of sea ice, and the available in situ observations remain relatively sparse. Nonetheless, with continuing global warming, increasingly large areas of the Arctic Ocean become seasonally ice‐free and can be observed with remote sensing. Here, we use satellite altimetry data acquired between 1993 and 2018 over the seasonally ice‐free Western Arctic Ocean to detect the signatures and hot spots of large mesoscale eddies, validating their detection method using independent optical and infrared satellite observations. The altimetry measurements were most frequent from July to October, revealing signatures of over 2000 individual eddies that were roughly equally partitioned between cyclones and anticyclones, with radii between 20 and 60 km and characteristic orbital velocities of about 0.05–0.4 m/s. A maximum number of eddies were detected in October in the Beaufort Sea and in November in the Chukchi Sea. The interannual variability of eddies in the Beaufort Sea is correlated with variations of intensity and freshwater content in the Beaufort Gyre. Using the regional eddy statistics, we discuss the potential eddy formation mechanisms over the key regions of their observations in the Amundsen Gulf, the area adjacent to the Mackenzie River mouth, the western part of the Beaufort Sea, and the Chukchi Sea.

Vertical Inhomogeneity Effect of Frozen Hydrometeor Habits in All‐Sky Passive Microwave Simulations

AbstractIn this study, we attempted to address the frozen hydrometeor habit vertical inhomogeneity (FHHVI) effects on microwave radiative transfer simulations. For simplification, we focused on a “two habits and two layers” FHHVI scheme in which two different ice habits can be assumed to be above and below a fixed habit boundary line of 325 hPa as suggested by studies of tropical cyclone vertical structure. The aforementioned FHHVI scheme was implemented into a radiative transfer for the Television Infrared Observation Satellite (TIROS) operational vertical sounder (RTTOV), which allows for close examination of the FHHVI effect on the radiative transfer process. Four scenarios (thin plate, dendrite, thin plate over dendrite, and dendrite over thin plate) were considered in the simulations at frequencies ranging from 10 to 183 GHz. We found that FHHVI has an obvious impact on the brightness temperature (BT) simulations. For real case studies, we considered the 2018 tropical cyclone Jebi in the Western Pacific basin. The hydrometeor profiles forecasted by the Global/Regional Assimilation and Prediction System (GRAPES) regional numerical weather prediction (NWP) model were used, and the simulated results were compared with observations from the instruments aboard on the Fengyun‐3D satellite, which recorded 61 overpasses during the life span of typhoon Jebi. We found that the overall performance of the BT simulations over multichannels could be improved by considering FHHVI effects. Specifically, the total penalty values that quantify the spectral consistency were 63.05 (dendrite), 43.88 (thin plate), 46.23 (dendrite over thin plate), and 39.03 (thin plate over dendrite), respectively.

Potential Impacts of Assimilating All-Sky Satellite Infrared Radiances on Convection-Permitting Analysis and Prediction of Tropical Convection

AbstractGeostationary infrared satellite observations are spatially dense [>1/(20 km)2] and temporally frequent (>1 h−1). These suggest the possibility of using these observations to constrain subsynoptic features over data-sparse regions, such as tropical oceans. In this study, the potential impacts of assimilating water vapor channel brightness temperature (WV-BT) observations from the geostationary Meteorological Satellite 7 (Meteosat-7) on tropical convection analysis and prediction were systematically examined through a series of ensemble data assimilation experiments. WV-BT observations were assimilated hourly into convection-permitting ensembles using Penn State’s ensemble square root filter (EnSRF). Comparisons against the independently observed Meteosat-7 window channel brightness temperature (Window-BT) show that the assimilation of WV-BT generally improved the intensities and locations of large-scale cloud patterns at spatial scales larger than 100 km. However, comparisons against independent soundings indicate that the EnSRF analysis produced a much stronger dry bias than the no data assimilation experiment. This strong dry bias is associated with the use of the simulated WV-BT from the prior mean during the EnSRF analysis step. A stochastic variant of the ensemble Kalman filter (NoMeanSF) is proposed. The NoMeanSF algorithm was able to assimilate the WV-BT without causing such a strong dry bias and the quality of the analyses’ horizontal cloud pattern is similar to EnSRF’s analyses. Finally, deterministic forecasts initiated from the NoMeanSF analyses possess better horizontal cloud patterns above 500 km than those of the EnSRF. These results suggest that it might be better to assimilate all-sky WV-BT through the NoMeanSF algorithm than the EnSRF algorithm.

The development of the Atmospheric Measurements by Ultra-Light Spectrometer (AMULSE) greenhouse gas profiling system and application for satellite retrieval validation

Abstract. We report in this paper the development of an embedded ultralight spectrometer (<3 kg) based on tuneable diode laser absorption spectroscopy (with a sampling rate of 24 Hz) in the mid-infrared spectral region. This instrument is dedicated to in situ measurements of the vertical profile concentrations of three main greenhouse gases – carbon dioxide (CO2), methane (CH4) and water vapour (H2O) – via standard weather and tethered balloons. The plug and play instrument is compact, robust, cost-effective, and autonomous. The instrument also has low power consumption and is non-intrusive. It was first calibrated during an in situ experiment on an ICOS (Integrated Carbon Observation System) site for several days, then used in two experiments with several balloon flights of up to 30 km altitude in the Reims region of France in 2017–2018 in collaboration with Météo-France CNRM (Centre National de Recherches Météorologiques). This paper shows the valuable interest of the data measured by the AMULSE (Atmospheric Measurements by Ultra-Light Spectrometer) instrument during the APOGEE (Atmospheric Profiles of Greenhouse Gases) measurement experiment, specifically for the vertical profiles of CO2 and CH4, measurements of which remain very sparse. We have carried out several experiments showing that the measured profiles have several applications: the validation of simulations of infrared satellite observations, evaluating the quality of chemical profiles from chemistry transport models (CTMs) and evaluating the quality of retrieved chemical profiles from the assimilation of infrared satellite observations. The results show that the simulations of infrared satellite observations from IASI (Infrared Atmospheric Sounding Interferometer) and CrIS (Cross-track Infrared Sounder) instruments performed in operational mode for numerical weather prediction (NWP) by the radiative transfer model (RTM) RTTOV (Radiative Transfer for the TIROS Operational Vertical Sounder) are of good quality. We also show that the MOCAGE (Modèle de Chimie Atmosphérique de Grande Echelle) and CAMS (Copernicus Atmospheric Monitoring Service) CTMs modelled ozone profiles fairly accurately and that the CAMS CTM represents the methane in the troposphere well compared to MOCAGE. Finally, the measured in situ ozone profiles allowed us to show the good quality of the retrieved ozone profiles by assimilating ozone-sensitive infrared spectral radiances from the IASI and CrIS.