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NASA’s ‘Time-Resolved Observations of Precipitation structure and storm Intensity with a Constellation of Smallsats’ (TROPICS) mission will consist of four 3U CubeSats, likely to be launched in May 2023. TROPICS will enable passive microwave observations of tropical cyclones with an unprecedented temporal resolution. Each satellite is equipped with cross-track scanning microwave sounders covering the 118 GHz oxygen and 183 GHz water vapor absorption bands as well as channels at 92 and 204 GHz. As a precursor to the full mission, the TROPICS Pathfinder satellite was launched in June 2021 and has provided valuable science data since August 2021. We will present a short overview on NASA’s TROPICS mission as well as a novel K-D-tree-based approach to derive temperature and moisture profiles alongside estimates of surface precipitation and ice water path over deep convection from TROPICS Pathfinder. In addition to the retrieved atmospheric parameters, the novel retrieval approach allows for an estimate of how likely it is for a given retrieval to be representative and for rigorous Bayesian uncertainty estimates associated with each parameter. Results will be presented for various tropical storms and compared against established precipitation products such as NASA’s Global Precipitation Measurement Mission (GPM) and the Global Precipitation Climatology Project (GPCP).

This paper describes three algorithms for retrieving precipitation over oceans from brightness temperatures (TBs) of the Micro-Wave Humidity Sounder-2 (MHWS-2) onboard Fengyun-3C (FY-3C). For algorithm development, scattering-induced TB depressions (ΔTBs) of MWHS-2 at channels between 89 and 190 GHz were collocated to rain rates derived from measurements of the Global Precipitation Measurement’s Dual-frequency Precipitation Radar (DPR) for the year 2017. ΔTBs were calculated by subtracting simulated cloud-free TBs from bias-corrected observed TBs for each channel. These ΔTBs were then related to rain rates from DPR using (1) multilinear regression (MLR); the other two algorithms, (2) range searches (RS) and (3) nearest neighbor searches (NNS), are based on k-dimensional trees. While all three algorithms produce instantaneous rain rates, the RS algorithm also provides the probability of precipitation and can be understood in a Bayesian framework. Different combinations of MWHS-2 channels were evaluated using MLR and results suggest that adding 118 GHz improves retrieval performance. The optimal combination of channels excludes high-peaking channels but includes 118 GHz channels peaking in the mid and high troposphere. MWHS-2 observations from another year were used for validation purposes. The annual mean 2.5° × 2.5° gridded rain rates from the three algorithms are consistent with those from the Global Precipitation Climatology Project (GPCP) and DPR. Their correlation coefficients with GPCP are 0.96 and their biases are less than 5%. The correlation coefficients with DPR are slightly lower and the maximum bias is ∼8%, partly due to the lower sampling density of DPR compared to that of MWHS-2.

The sensitivity of microwave brightness temperatures (TBs) to hydrometeors at frequencies between 89 and 190 GHz is investigated by comparing Fengyun-3C (FY-3C) Microwave Humidity Sounder-2 (MWHS-2) measurements with radar reflectivity profiles and retrieved products from the Global Precipitation Measurement mission’s Dual-Frequency Precipitation Radar (DPR). Scattering-induced TB depressions (ΔTBs), calculated by subtracting simulated cloud-free TBs from bias-corrected observed TBs for each channel, are compared with DPR-retrieved hydrometeor water path (HWP) and vertically integrated radar reflectivity ZINT. We also account for the number of hydrometeors actually visible in each MWHS-2 channel by weighting HWP with the channel’s cloud-free gas transmission profile and the observation slant path. We denote these transmission-weighted, slant-path-integrated quantities with a superscript asterisk (e.g., HWP*). The so-derived linear sensitivity of ΔTB with respect to HWP* increases with frequency roughly to the power of 1.78. A retrieved HWP* of 1 kg m−2 at 89 GHz on average corresponds to a decrease in observed TB, relative to a cloud-free background, of 11 K. At 183 GHz, the decrease is about 34–53 K. We perform a similar analysis using the vertically integrated, transmission-weighted slant-path radar reflectivity and find that ΔTB also decreases approximately linearly with . The exponent of 0.58 corresponds to the one we find in the purely DPR-retrieval-based ZINT–HWP relation. The observed sensitivities of ΔTB with respect to and HWP* allow for the validation of hydrometeor scattering models.

Spectroscopy plays a key role in Earth observations, especially for studies involving vegetation function and structure. These measurements are critical in the context of carbon cycle monitoring fr...

The 15 January 2022 eruption of Hunga Tonga-Hunga Ha’apai, and the preceding eruptions on 19 December 2021 and 13 January 2022, were remarkable, partly because the eruptions generated extensive umbrella clouds, regions where the volcanic clouds spread laterally. Here we use satellite remote sensing to evaluate the umbrella cloud tops’ heights, longevities, water contents, and volumetric flow rates. We identified two umbrella clouds at distinct elevations on 15 January 2022. Specifically, after 05:30 UTC, the strong westward propagation of an upper umbrella cloud at 31 km ± 3 km enabled the visibility of the lower umbrella cloud at 17 km ± 2 km. The satellite-derived volumetric flow rate for 15 January 2022 was ~5.0 × 1011 m3 s−1, nearly two orders of magnitude higher than the volumetric flow rates estimated for the 19 December 2021 and 13 January 2022 eruptions. Finally, we found that the umbrellas on all three dates were ice-rich.

The attached data files and supplementary movies are related to our manuscript, "Eruption chronology of the December 2021 to January 2022 Hunga Tonga-Hunga Ha'apai eruption sequence", in Nature Communications: Earth & Environmental Sciences. Thank you. &nbsp

We use the CloudSat 2006–2016 data record to estimate snowfall over the Greenland Ice Sheet (GrIS). We first evaluate CloudSat snowfall retrievals with respect to remaining ground-clutter issues. Comparing CloudSat observations to the GrIS topography (obtained from airborne altimetry measurements during IceBridge) we find that at the edges of the GrIS spurious high-snowfall retrievals caused by ground clutter occasionally affect the operational snowfall product. After correcting for this effect, the height of the lowest valid CloudSat observation is about 1200 m above the local topography as defined by IceBridge. We then use ground-based millimeter wavelength cloud radar (MMCR) observations obtained from the Integrated Characterization of Energy, Clouds, Atmospheric state, and Precipitation at Summit, Greenland (ICECAPS) experiment to devise a simple, empirical correction to account for precipitation processes occurring between the height of the observed CloudSat reflectivities and the snowfall near the surface. Using the height-corrected, clutter-cleared CloudSat reflectivities we next evaluate various Z–S relationships in terms of snowfall accumulation at Summit through comparison with weekly stake field observations of snow accumulation available since 2007. Using a set of three Z–S relationships that best agree with the observed accumulation at Summit, we then calculate the annual cycle snowfall over the entire GrIS as well as over different drainage areas and compare the derived mean values and annual cycles of snowfall to ERA-Interim reanalysis. We find the annual mean snowfall over the GrIS inferred from CloudSat to be 34±7.5 cm yr−1 liquid equivalent (where the uncertainty is determined by the range in values between the three different Z–S relationships used). In comparison, the ERA-Interim reanalysis product only yields 30 cm yr−1 liquid equivalent snowfall, where the majority of the underestimation in the reanalysis appears to occur in the summer months over the higher GrIS and appears to be related to shallow precipitation events. Comparing all available estimates of snowfall accumulation at Summit Station, we find the annually averaged liquid equivalent snowfall from the stake field to be between 20 and 24 cm yr−1, depending on the assumed snowpack density and from CloudSat 23±4.5 cm yr−1. The annual cycle at Summit is generally similar between all data sources, with the exception of ERA-Interim reanalysis, which shows the aforementioned underestimation during summer months.

International audience; SAPHIR-NG is a new generation atmospheric sounder concept based on the existing SAPHIR instrument, onboard Megha-Tropiques, which has been operating since 2011. Its improvements with respect to SAPHIR are presented hereafter, both in terms of scientific objectives and instrumental architecture.

The retrieval of the mass-weighted mean diameter (Dm) is a fundamental component of spaceborne precipitation retrievals. The Dual-Frequency Precipitation Radar (DPR) on the Global Precipitation Measurement (GPM) satellite is the first satellite to use Dual Wavelength Ratio measurements - the quotient of radar reflectivity factors (Z) measured at Ku and Ka wavelengths – to retrieve Dm. While it is established that DWR, being theoretically insensitive to changes in ice crystal mass and concentration, can provide a superior retrieval of Dm compared to Z based retrievals, the benefits of this retrieval have yet to be directly observed or quantified. In this study, DWR-Dm and Z-Dm relationships are empirically generated from collocated airborne radar and in-situ cloud particle probe measurements. Data is collected during nine intensive observation periods (IOPs) from three experiments representing different locations and times of year. Across IOPs with varying ice crystal concentrations, cloud temperatures, and storm types, Z-Dm relationships vary considerably while the DWR-Dm relationship remains consistent. This study confirms that a DWR-Dm relationship can provide a more accurate and consistent Dm retrieval than a Z-Dm relationship, quantified by a reduced overall RMSE (0.19 mm and 0.25 mm, respectively) and a reduced range of biases between experiments (0.11 mm and 0.32 mm, respectively).

Remote sensing offers a non-destructive method to detect plant physiological response to the environment by measuring chlorophyll fluorescence (CF). Most methods to estimate CF require relatively complex retrieval, spectral fitting, or modelling methods. An investigation was undertaken to evaluate measurements of CF using a relatively straightforward technique to detect and monitor plant stress with a spectroradiometer and blue-red light emitting diode (LED). CF spectral response of tomato plants treated with a photosystem inhibitor were assessed and compared to traditional reflectance-based indices: normalized difference vegetation index (NDVI) and photochemical reflectance index (PRI). The blue-red LEDs provided input irradiance and a “window” in the CF emission range of plants (~650 to 850 nm) sufficient to capture distinctive “two-peak” spectra and to distinguish plant health from day to day of the experiment, while within day differences were noisy. CF-based metrics calculated from CF spectra clearly captured signs of vegetation stress earlier than reflectance-based indices and by visual inspection. This CF monitoring technique is a flexible and scalable option for collecting plant function data, especially for indicating early signs of stress. The technique can be applied to a single plant or larger canopies using LED in dark conditions by an individual, or a manned or unmanned vehicle for agricultural or military purposes.

Marine stratocumulus (Sc) clouds play an essential role in the earth radiation budget. Here, we compare liquid water path (LWP), cloud optical thickness (τ), and cloud droplet effective radius (re) retrievals from 2 years of collocated Spinning Enhanced Visible and Infrared Imager (SEVIRI), Moderate Resolution Imaging Spectroradiometer (MODIS), and Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) observations; estimate the effect of biomass burning smoke on passive imager retrievals; and evaluate the diurnal cycle of South Atlantic marine Sc clouds.The effect of absorbing aerosols from biomass burning on the retrievals was investigated using the aerosol index (AI) obtained from the Ozone Monitoring Instrument (OMI). SEVIRI and MODIS LWPs were found to decrease with increasing AI relative to TMI LWP, consistent with well-known negative visible/near-infrared (VIS/NIR) retrieval biases in τ and re. In the aerosol-affected months of July–August–September, SEVIRI LWP – based on the 1.6 µm re – was biased low by 14 g m−2 ( ∼ 16 %) compared to TMI in overcast scenes, while MODIS LWP showed a smaller low bias of 4 g m−2 ( ∼ 5 %) for the 1.6 µm channel and a high bias of 8 g m−2 ( ∼ 10 %) for the 3.7 µm channel compared to TMI. Neglecting aerosol-affected pixels reduced the mean SEVIRI–TMI LWP bias considerably. For 2 years of data, SEVIRI LWP had a correlation with TMI and MODIS LWP of about 0.86 and 0.94, respectively, and biases of only 4–8 g m−2 (5 %–10 %) for overcast cases.The SEVIRI LWP diurnal cycle was in good overall agreement with TMI except in the aerosol-affected months. Both TMI and SEVIRI LWP decreased from morning to late afternoon, after which a slow increase was observed. Terra and Aqua MODIS mean LWPs also suggested a similar diurnal variation. The relative amplitude of the 2-year-mean and seasonal-mean LWP diurnal cycle varied between 35 % and 40 % from morning to late afternoon for overcast cases. The diurnal variation in SEVIRI LWP was mainly due to changes in τ, while re showed only little diurnal variability.

The first extended comprehensive data set of the retrieval uncertainties in passive microwave observations of cloud liquid water path (CLWP) for warm oceanic clouds has been created for practical use in climate applications. Four major sources of systematic errors were considered over the 9-year record of the Advanced Microwave Scanning Radiometer-EOS (AMSR-E): clear-sky bias, cloud-rain partition (CRP) bias, cloud-fraction-dependent bias, and cloud temperature bias. Errors were estimated using a unique merged AMSR-E/Moderate resolution Imaging Spectroradiometer Level 2 data set as well as observations from the Cloud-Aerosol Lidar with Orthogonal Polarization and the CloudSat Cloud Profiling Radar. To quantify the CRP bias more accurately, a new parameterization was developed to improve the inference of CLWP in warm rain. The cloud-fraction-dependent bias was found to be a combination of the CRP bias, an in-cloud bias, and an adjacent precipitation bias. Globally, the mean net bias was 0.012 kg/m2, dominated by the CRP and in-cloud biases, but with considerable regional and seasonal variation. Good qualitative agreement between a bias-corrected AMSR-E CLWP climatology and ship observations in the Northeast Pacific suggests that the bias estimates are reasonable. However, a possible underestimation of the net bias in certain conditions may be due in part to the crude method used in classifying precipitation, underscoring the need for an independent method of detecting rain in warm clouds. This study demonstrates the importance of combining visible-infrared imager data and passive microwave CLWP observations for estimating uncertainties and improving the accuracy of these observations.

(Beginning of WHAT, WHEN, WHERE Summary Box:) What: The work-shop gathered almost 50 scientists from Europe and the United States to discuss the progress towards developing electromagnetic scattering databases for ice and snow particles in the microwave region, their applications, the physical approximations used to compute these scattering properties, and how remote sensing and in situ observations can be used to validate scattering datasets. One of the main priorities of the workshop was to foster communication between users and developers of scattering databases, and to define standards and conventions for scattering data structures and variables. When: 28-30 June 2017. Where: Cologne, Germany (END of what, when, where summary box).

The International Precipitation Working Group (IPWG) is a permanent International Science Working Group (ISWG) of the Coordination Group for Meteorological Satellites (CGMS), co-sponsored by CGMS and the World Meteorological Organization (WMO). The IPWG provides a focal point and forum for the international scientific community to address the issues and challenges of satellite-based quantitative precipitation retrievals, and for the operational agencies to access and make use of precipitation products. Through partnerships and biennial meetings, the group supports the exchange of information on techniques for retrieving and measuring precipitation and for enhancing the impact of space-borne precipitation retrievals in numerical weather and hydrometeorological prediction and climate studies. The group furthers the refinement of current estimation techniques and the development of new methodologies for improved global precipitation measurements, together with the validation of the derived precipitation products with ground-based precipitation measurements. The IPWG identifies critical issues, provides recommendations to the CGMS and supports upcoming precipitation-oriented missions. Training activities on precipitation retrieval from space are also part of the IPWG mandate in cooperation with WMO and other bodies.

The Multi-Sensor Advanced Climatology of Liquid Water Path (MAC-LWP), an updated and enhanced version of the University of Wisconsin (UWisc) cloud liquid water path (CLWP) climatology, currently provides 29 years (1988 – 2016) of monthly gridded (1°) oceanic CLWP information constructed using Remote Sensing Systems (RSS) inter-calibrated 0.25°-resolution retrievals. Satellite sources include SSM/I, TMI, AMSR-E, WindSat, SSMIS, AMSR-2 and GMI. To mitigate spurious CLWP trends, the climatology is corrected for drifting satellite overpass times by simultaneously solving for the monthly average CLWP and monthly-mean diurnal cycle. In addition to a longer record and six additional satellite products, major enhancements relative to the UWisc climatology include updating the input to version 7 RSS retrievals, a correction for a CLWP bias (based on matchups to clear-sky MODIS scenes), and the construction of a total (cloud+rain) liquid water path (TLWP) record for use in analyses of columnar liquid water in raining clouds. Because the microwave emission signal from cloud water is similar to that of precipitation-sized hydrometeors, greater uncertainty in the CLWP record is expected in regions of substantial precipitation. Therefore, the TLWP field can also be used as a quality-control screen, where uncertainty increases as the ratio of CLWP to TLWP decreases. For regions where confidence in CLWP is highest (i.e. CLWP:TLWP > 0.8), systematic differences in MAC CLWP relative to UWisc CLWP range from −15% (e.g. global oceanic stratocumulus decks) to +5-10% (e.g. portions of the higher-latitudes, storm tracks, and shallower convection regions straddling the ITCZ). The dataset is currently hosted at the Goddard Earth Science Data and Information Services Center (http://disc.sci.gsfc.nasa.gov).

The microwave radiometers (MWRs) on board the European Remote Sensing Satellites 1 and 2 (ERS-1 and ERS-2) and Envisat provide a continuous time series of brightness temperature observations between 1991 and 2012. Here we report on a new total column water vapour (TCWV) and wet tropospheric correction (WTC) dataset that builds on this time series. We use a one-dimensional variational approach to derive TCWV from MWR observations and ERA-Interim background information. A particular focus of this study lies on the intercalibration of the three different instruments, which is performed using constraints on liquid water path (LWP) and TCWV. Comparing our MWR-derived time series of TCWV against TCWV derived from Global Navigation Satellite System (GNSS) we find that the MWR-derived TCWV time series is stable over time. However, observations potentially affected by precipitation show a degraded performance compared to precipitation-free observations in terms of the accuracy of retrieved TCWV. An analysis of WTC shows further that the retrieved WTC is superior to purely ERA-Interim-derived WTC for all satellites and for the entire time series. Even compared to the European Space Agency's (ESA) operational WTC retrievals, which incorporate in addition to MWR additional observational data, the here-described dataset shows improvements in particular for the mid-latitudes and for the two earlier satellites, ERS-1 and ERS-2. The dataset is publicly available under doi:10.5676/DWD_EMIR/V001 (Bennartz et al., 2016).

The first observationally based near-global shallow cumuliform snowfall census is undertaken using multiyear CloudSat Cloud Profiling Radar observations. CloudSat snowfall observations and snowfall rate estimates from the CloudSat 2C-Snow Water Content and Snowfall Rate (2C-SNOW-PROFILE) product are partitioned between shallow cumuliform and nimbostratus cloud structures by utilizing coincident cloud category classifications from the CloudSat 2B-Cloud Scenario Classification (2B-CLDCLASS) product. Shallow cumuliform (nimbostratus) snowfall events comprise about 36% (59%) of snowfall events in the CloudSat snowfall dataset. The remaining 5% of snowfall events are distributed between other categories. Distinct oceanic versus continental trends exist between the two major snowfall categories, as shallow cumuliform snow-producing clouds occur predominantly over the oceans. Regional differences are also noted in the partitioned dataset, with over-ocean regions near Greenland, the far North Atlantic Ocean, the Barents Sea, the western Pacific Ocean, the southern Bering Sea, and the Southern Hemispheric pan-oceanic region containing distinct shallow snowfall occurrence maxima exceeding 60%. Certain Northern Hemispheric continental regions also experience frequent shallow cumuliform snowfall events (e.g., inland Russia), as well as some mountainous regions. CloudSat-generated snowfall rates are also partitioned between the two major snowfall categories to illustrate the importance of shallow snow-producing cloud structures to the average annual snowfall. While shallow cumuliform snowfall produces over 50% of the annual estimated surface snowfall flux regionally, about 18% (82%) of global snowfall is attributed to shallow (nimbostratus) snowfall. This foundational spaceborne snowfall study will be utilized for follow-on evaluative studies with independent model, reanalysis, and ground-based observational datasets to characterize respective dataset biases and to better quantify CloudSat snowfall detection and quantitative snowfall estimate uncertainties.

Recent technology advances in miniature microwave radiometers that can be hosted on very small satellites has made possible a new class of affordable constellation missions that provide very high revisit rates of tropical cyclones and other severe weather. The Time-Resolved Observations of Precipitation structure and storm Intensity with a Constellation of Smallsats (TROPICS) mission was selected by NASA as part of the Earth Venture-Instrument (EVI-3)program and is now in development with planned launch readiness in late 2019. The overarching goal for TROPICS is to provide nearly all-weather observations of 3-D temperature and humidity, as well as cloud ice and precipitation horizontal structure, at high temporal resolution to conduct high-value science investigations of tropical cyclones (TCs). TROPICS will provide rapid-refresh microwave measurements (median refresh rate better than 60 minutes for the baseline mission) over the tropics that can be used to observe the thermodynamics of the troposphere and precipitation structure for storm systems at the mesoscale and synoptic scale over the entire storm life cycle. TROPICS will comprise a constellation of at least six CubeSats in three low-Earth orbital planes. Each CubeSat will host a high performance radiometer to provide temperature profiles using seven channels near the 118.75 GHz (gigahertz) oxygen absorption line, water vapor profiles using three channels near the 183 GHz water vapor absorption line, imagery in a single channel near 90 GHz for precipitation measurements (when combined with higher resolution water vapor channels), and a single channel at 205 GHz that is more sensitive to precipitation-sized ice particles and low-level moisture. This observing system offers an unprecedented combination of horizontal and temporal resolution in the microwave spectrum to measure environmental and inner-core conditions for TCs on a nearly global scale and is a major leap forward in the temporal resolution of several key parameters needed for assimilation into advanced data assimilation systems capable of utilizing rapid-update radiance or retrieval data. Here, we provide an overview of the mission and an update on current status,with a focus on unique characteristics of the Cubesat system, recent performance simulations on a range of observables to be provided by the constellation, and a summary of science applications.

The cloud droplet number concentration (N d) is of central interest to improve the understanding of cloud physics and for quantifying the effective radiative forcing by aerosol‐cloud interactions. Current standard satellite retrievals do not operationally provide N d, but it can be inferred from retrievals of cloud optical depth (τ c) cloud droplet effective radius (r e) and cloud top temperature. This review summarizes issues with this approach and quantifies uncertainties. A total relative uncertainty of 78% is inferred for pixel‐level retrievals for relatively homogeneous, optically thick and unobscured stratiform clouds with favorable viewing geometry. The uncertainty is even greater if these conditions are not met. For averages over 1° ×1° regions the uncertainty is reduced to 54% assuming random errors for instrument uncertainties. In contrast, the few evaluation studies against reference in situ observations suggest much better accuracy with little variability in the bias. More such studies are required for a better error characterization. N d uncertainty is dominated by errors in r e, and therefore, improvements in r e retrievals would greatly improve the quality of the N d retrievals. Recommendations are made for how this might be achieved. Some existing N d data sets are compared and discussed, and best practices for the use of N d data from current passive instruments (e.g., filtering criteria) are recommended. Emerging alternative N d estimates are also considered. First, new ideas to use additional information from existing and upcoming spaceborne instruments are discussed, and second, approaches using high‐quality ground‐based observations are examined., Key Points Satellite cloud droplet concentration uncertainties of 78% for pixel‐level retrievals and 54% for 1 by 1 degree retrievals are estimatedThe effective radius retrieval is the most important aspect for improvement, and more in situ evaluation is neededPotential improvements using passive and active satellite, and ground‐based instruments are discussed

Marine stratocumulus (Sc) clouds play an essential role in the earth radiation budget. Here, we compare liquid water path (LWP), optical thickness (COT), and effective radius (CER) retrievals from two years of collocated Spinning Enhanced Visible and InfraRed Imager (SEVIRI), MODerate resolution Imaging Spectroradiometer (MODIS), and Tropical Rainfall Measuring Mission Microwave Imager (TMI) observations, estimate the effect of biomass burning smoke on passive imager retrievals, as well as evaluate the diurnal cycle of South Atlantic marine Sc clouds. The effect of absorbing aerosols from biomass burning on the retrievals was investigated using aerosol index (AI) obtained from the Ozone Monitoring Instrument (OMI). SEVIRI and MODIS LWPs were found to decrease with increasing AI relative to TMI LWP, consistent with well-known negative visible/near-infrared retrieval biases in COT and CER. In the aerosol-affected months of July–August–September, SEVIRI LWP – based on the 1.6-µm CER – was biased low by 14 g m−2 (~ 16 %) compared to TMI in overcast scenes, while MODIS LWP showed a smaller low bias of 4 g m−2 (~ 5 %) for the 1.6-µm channel and a high bias of 8 g m−2 (~ 10 %) for the 3.7-µm channel compared to TMI. Neglecting aerosol-affected pixels reduced the mean SEVIRI-TMI LWP bias considerably. On a two-year data base, SEVIRI LWP had a correlation with TMI and MODIS LWP of about 0.86 and 0.94, respectively, and biases of only 4–8 g m−2 (5–10 %) for overcast cases. The SEVIRI LWP diurnal cycle was in good overall agreement with TMI except in the aerosol-affected months. Both TMI and SEVIRI LWP decreased from morning to late afternoon, after which a slight increase was observed. Terra and Aqua MODIS mean LWPs also suggested a similar diurnal variation. The relative amplitude of the two-year mean and seasonal mean LWP diurnal cycle varied between 35–40 % from morning to late afternoon for overcast cases. The diurnal variation in SEVIRI LWP was mainly due to changes in COT, while CER showed only little diurnal variability.

Within the context of the Air Quality Model Evaluation International Initiative Phase 2 (AQMEII2) project, this part II paper performs a multi-model assessment of major column abundances of gases, radiation, aerosol, and cloud variables for 2006 and 2010 simulations with three online-coupled air quality models over the North America using available satellite data. It also provides the first comparative assessment of the capabilities of the current generation of online-coupled models in simulating column variables. Despite the use of different model configurations and meteorological initial and boundary conditions, most simulations show comparable model performance for many variables. The evaluation results show an excellent agreement between all simulations and satellite-derived radiation variables including downward surface solar radiation, longwave radiation, and top-of-atmospheric outgoing longwave radiation, as well as precipitable water vapor with domain-average normalized mean biases (NMBs) of typically less than 5% and correlation coefficient (R) typically more than 0.9. Most simulations perform well for column-integrated abundance of CO with domain-average NMBs of −9.4% to −2.2% in 2006 and −12.1% to 4.6% in 2010 and from reasonably well to fair for column NO2, HCHO, and SO2, with domain-average NMBs of −37.7% to 2.1%, −27.3% to 59.2%, and 16.1% to 114.2% in 2006, respectively, and, 12.9% to 102.1%, −25.0% to 87.6%, −65.2% to 7.4% in 2010, respectively. R values are high for CO and NO2 typically between 0.85 and 0.9 (i.e., R2 of 0.7–0.8). Tropospheric ozone residuals are overpredicted by all simulations due to overestimates of ozone profiles from boundary conditions. Model performance for cloud-related variables is mixed and generally worse compared to gases and radiation variables. Cloud fraction (CF) is well reproduced by most simulations. Other aerosol/cloud related variables such as aerosol optical depth (AOD), cloud optical thickness, cloud liquid water path, cloud condensation nuclei, and cloud droplet number concentration (CDNC) are moderately to largely underpredicted by most simulations, due to underpredictions of aerosol loadings and also indicating high uncertainties associated with the current model treatments of aerosol–cloud interactions and the need for further model development. Negative correlations are found for AOD for most simulations due to large negative biases over the western part of the domain. Inter-model discrepancies also exist for a few variables such as column abundances of HCHO and SO2 and CDNC due likely to different chemical mechanisms, biogenic emissions, and treatments of aerosol indirect effects. Most simulations can also capture the inter-annual trend observed by satellites between 2006 and 2010 for several variables such as column abundance of NO2, AOD, CF, and CDNC. Results shown in this work provide the important benchmark for future online-couple air quality model development.

A novel method for classifying Arctic precipitation using ground based remote sensors is presented. Using differences in the spectral variation of microwave absorption and scattering properties of cloud liquid water and ice, this method can distinguish between different types of snowfall events depending on the presence or absence of condensed liquid water in the clouds that generate the precipitation. The classification reveals two distinct, primary regimes of precipitation over the Greenland Ice Sheet (GIS): one originating from fully glaciated ice clouds and the other from mixed-phase clouds. Five years of co-located, multi-instrument data from the Integrated Characterization of Energy, Clouds, Atmospheric state, and Precipitation at Summit (ICECAPS) are used to examine cloud and meteorological properties and patterns associated with each precipitation regime. The occurrence and accumulation of the precipitation regimes are identified and quantified. Cloud and precipitation observations from additional ICECAPS instruments illustrate distinct characteristics for each regime. Additionally, reanalysis products and back-trajectory analysis show different synoptic- scale forcings associated with each regime. Precipitation over the central GIS exhibits unique microphysical characteristics due to the high surface elevations as well as connections to specific large-scale flow patterns. Snowfall originating from the ice clouds is coupled to deep, frontal cloud systems advecting up and over the southeast Greenland coast to the central GIS. These events appear to be associated with individual storm systems generated by low pressure over Baffin Bay and Greenland lee cyclogenesis. Snowfall originating from mixed-phase clouds is shallower and has characteristics typical of supercooled cloud liquid water layers, and slowly propagates from the south and southwest Greenland along a quiescent flow above the GIS.

The Time-Resolved Observations of Precipitation structure and storm Intensity with a Constellation of Smallsats (TROPICS) mission was selected by NASA as part of the Earth Venture-Instrument (EVI-3) program. The overarching goal for TROPICS is to provide nearly all-weather observations of 3D temperature and humidity, as well as cloud ice and precipitation horizontal structure, at high temporal resolution to conduct high-value science investigations of tropical cyclones. TROPICS will provide rapid-refresh microwave measurements (median refresh rate better than 60 min for the baseline mission) which can be used to observe the thermodynamics of the troposphere and precipitation structure for storm systems at the mesoscale and synoptic scale over the entire storm life cycle. TROPICS comprises six Cube-Sats in three low-Earth orbital planes. Each CubeSat will host a high-performance radiometer to provide temperature profiles using seven channels near the 118.75 GHz oxygen absorption line, water vapour profiles using three channels near the 183 GHz water vapour absorption line, imagery in a single channel near 90 GHz for precipitation measurements (when combined with higher-resolution water vapour channels), and a single channel near 205 GHz which is more sensitive to precipitation-sized ice particles. This observing system offers an unprecedented combination of horizontal and temporal resolution to measure environmental and inner-core conditions for tropical cyclones on a nearly global scale and is a major leap forward in the temporal resolution of several key parameters needed for assimilation into advanced data assimilation systems capable of utilizing rapid-update radiance or retrieval data.Launch readiness is currently projected for late 2019.

An observation-based study is presented that utilizes aircraft data from the 2003 Wakasa Bay Advanced Microwave Scanning Radiometer Precipitation Validation Campaign to assess recent advances in the modeling of microwave scattering properties of nonspherical ice particles in the atmosphere. Previous work has suggested that a triple-frequency (Ku–Ka–W band) reflectivity framework appears capable of identifying key microphysical properties of snow, potentially providing much-needed constraints on significant sources of uncertainty in current snowfall retrieval algorithms used for microwave remote sensing instruments. However, these results were based solely on a modeling framework. In contrast, this study considers the triple-frequency approach from an observational perspective using airborne radar observations from the Wakasa Bay field campaign. After accounting for several challenges with the observational dataset, such as beam mismatching and attenuation, observed dual-wavelength ratio results are presented that confirm both the utility of a multifrequency approach to snowfall retrieval and the validity of the unique signatures predicted by complex aggregate ice particle scattering models. This analysis provides valuable insight into the microphysics of frozen precipitation that can in turn be applied to more readily available single- and dual-frequency systems, providing guidance for future precipitation retrieval algorithms.

Global food security is becoming increasingly stressed by growing populations and climate change. To compensate for these stresses, crop yields must increase throughout the upcoming century. One of the more prominently featured solutions entails genetically modified crops, but their impacts on yields are contested. Here, we leverage machine learning techniques to examine the effects genetically modified crops have had on US corn yields. In particular, a principal components analysis conducted on US Midwest county yields reveals that the commercialization of genetically modified corn accentuated preexisting spatial disparities in production and explains approximately 6–12% of the region's inter-county variation in yields from 1980 to 2015. Additionally, counterfactual yield trajectories predicted by Bayesian structural time series models using non-genetically modified crops as synthetic controls suggest that the adoption of this biotechnology amounted to an approximate 13% increase in overall US corn yields from 1996 to 2015.

Solar-induced fluorescence (SIF) has been empirically linked to gross primary productivity (GPP) in multiple ecosystems and is thus a promising tool to address the current uncertainties in carbon fluxes at ecosystem to continental scales. However, studies utilizing satellite-measured SIF in South America have concentrated on the Amazonian tropical forest, while SIF in other regions and vegetation classes remain uninvestigated. We examined three years of Orbiting Carbon Observatory-2 (OCO-2) SIF data for vegetation classes within and across the six Brazilian biomes (Amazon, Atlantic Forest, Caatinga, Cerrado, Pampa, and Pantanal) to answer the following: (1) how does satellite-measured SIF differ? (2) What is the relationship (strength and direction) of satellite-measured SIF with canopy temperature (Tcan), air temperature (Tair), and vapor pressure deficit (VPD)? (3) How does the phenology of satellite-measured SIF (duration and amplitude of seasonal integrated SIF) compare? Our analysis shows that OCO-2 captures a significantly higher mean SIF with lower variability in the Amazon and lower mean SIF with higher variability in the Caatinga compared to other biomes. OCO-2 also distinguishes the mean SIF of vegetation types within biomes, showing that evergreen broadleaf (EBF) mean SIF is significantly higher than other vegetation classes (deciduous broadleaf (DBF), grassland (GRA), savannas (SAV), and woody savannas (WSAV)) in all biomes. We show that the strengths and directions of correlations of OCO-2 mean SIF to Tcan, Tair, and VPD largely cluster by biome: negative in the Caatinga and Cerrado, positive in the Pampa, and no correlations were found in the Pantanal, while results were mixed for the Amazon and Atlantic Forest. We found mean SIF most strongly correlated with VPD in most vegetation classes in most biomes, followed by Tcan. Seasonality from time series analysis reveals that OCO-2 SIF measurements capture important differences in the seasonal timing of SIF for different classes, details masked when only examining mean SIF differences. We found that OCO-2 captured the highest base integrated SIF and lowest seasonal pulse integrated SIF in the Amazon for all vegetation classes, indicating continuous photosynthetic activity in the Amazon exceeds other biomes, but with small seasonal increases. Surprisingly, Pantanal EBF SIF had the highest total integrated SIF of all classes in all biomes due to a large seasonal pulse. Additionally, the length of seasons only accounts for about 30% of variability in total integrated SIF; thus, integrated SIF is likely captures differences in photosynthetic activity separate from structural differences. Our results show that satellite measurements of SIF can distinguish important functioning and phenological differences in vegetation classes and thus has the potential to improve our understanding of productivity and seasonality in the tropics.

k-distribution methods are efficient tools for large spectral band radiation transfer simulations in the atmosphere. A weakness of classical k-distribution method (correlated k-distribution method) is the simulation of layered atmosphere with variable gas absorption. This weakness is due to the use of the correlation approximation. We can avoid the use of the correlation approximation using a spectral mapping for the construction of the k-distribution intervals. This already-existing method is known as Spectral Mapping Transformation (SMT). Within SMT k-distribution, k-distribution is built to suit all the atmospheric layers. This difference of method, compared to the correlated k-distribution methods, leads to a loss of rapidity but to a gain of precision. Particularly, SMT k-distribution methods permit a balance between rapidity and precision by decreasing or increasing the number of bins (spectroscopically similar k-distribution intervals). Within this article, we recapitulate the main principles of SMT k-distribution methods and present two improvements: the k-IR method, which is an adaptation of an SMT k-distribution method to the thermal infrared, and k-bin, which is an adaptation of an SMT k-distribution method that allows the user to simulate narrow-band satellite channels with a non-fixed spectral response function. k-IR permits a modeling of the absorption and emission by gases, even if the spectral variability of the Planck function is completely different from the spectral variability of absorption/emission coefficients. This is achieved by associating to each bin (spectroscopically similar k-distribution interval) a precomputed value of the bin-averaged blackbody radiance. Within this paper, we outline k-IR and apply it for the simulation of Channel 3 (12.05 μm) of the Imaging Infrared Radiometer (IIR) aboard the CALIPSO satellite. Results show that the accuracy steadily increases with an increasing number of bins. This will allow us to reach the instrument precision. k-bin, otherwise, is a more stringent approach of the k-distribution method. The major difference between a conventional k-distribution and k-bin is that, for a given spectral interval no assumption about the shape of the sensor weighting function has to be incorporated a priori. For a given spectral interval, any sensor response function can be constructed from a set of radiative transfer simulations. This requires somewhat different constraints in the way the subdivision in bins is performed, namely we must ensure that not only the band-averaged transmission is resembled to high accuracy, but also the transmission in each bin. Within this paper we outline k-bin method and apply it to high-resolution spectroscopic simulations in the oxygen A-band. k-bin allows for a representation of any channel (with a resolution of 0.04 nm or lower) within the oxygen A-band with an accuracy of 0.2% or better with less than 600 simulations. This corresponds for the Orbiting Carbon Observatory-2 (OCO-2) to a computational cost of 0.6 simulations per channel.

Stratocumulus (Sc) cloud cover is a persistent feature of the subtropical North and South Atlantic. It is well known that Sc cloud cover increases with decreasing temperatures of the underlying sea surface and that an increase in cloud cover will cool the surface temperatures via increasing the local albedo, otherwise known as the Sc feedback. In this study observations are used to quantify the magnitude and spatial structure of the Sc feedback in the tropical–extratropical Atlantic Ocean and investigate the role of the Sc feedback in shaping the evolution of coupled modes of variability there. The authors show that in the Atlantic the Sc feedback increases the time scales of Newtonian cooling by 40% and in an idealized linear model of the tropical Atlantic the dominant mode of coupled variability (the Atlantic meridional mode or dipole mode) would experience no transient growth without the influence of the Sc feedback on the surface temperature damping time scales. This study also investigates Atlantic Sc clouds and the Sc feedback in phase 3 of the Coupled Model Intercomparison Project (CMIP3) models. The authors find that most models have negative biases in the mean state of Sc cloud cover and do not reproduce the observed spatial structure of Atlantic Sc clouds. This study also shows that while the majority of models exhibit some agreement with observations in the meridional structure of the Sc feedback, the vast majority of models underestimate the dependence of Sc cloud cover on the underlying SST.

We present and evaluate a climatology of cloud droplet number concentration (CDNC) based on 13 years of Aqua-MODIS observations. The climatology provides monthly mean 1 × 1 degree CDNC values plus associated uncertainties. All values are in-cloud values, that is, if the grid box is covered to 10 % with clouds, then the reported CDNC value will be valid for the cloudy part of the grid-box. Here, we provide an overview on how the climatology was generated and assess and quantify potential systematic error sources including effects of broken clouds, and remaining artefacts caused by the retrieval process or related to observation geometry. Retrievals and evaluations were performed at the scale of initial MODIS observations (in contrast to some earlier climatologies, which were created based on already gridded data). This allowed us to implement additional screening criteria, so that observations inconsistent with key assumptions made in the CDNC retrieval could be rejected. Application of these additional screening criteria led to significant changes in the annual cycle of CDNC both in terms of its phase and magnitude. After an optimal screening was established a final CDNC climatology was generated. Resulting CDNC uncertainties for the climatology are in the order of 30 % in the stratocumulus regions and 60 % to 80 % elsewhere. The climatology is available in Network Common Data Format (netCDF) and adheres to the Climate and Forecast (CF) convention. The climatology is available via Digital Object Identifier (Bennartz and Rausch, 2016, doi:10.15695/vudata.ees.1).

Cloud and atmospheric properties strongly influence the mass and energy budgets of the Greenland Ice Sheet (GIS). To address critical gaps in the understanding of these systems, a new suite of cloud- and atmosphere-observing instruments has been installed on the central GIS as part of the Integrated Characterization of Energy, Clouds, Atmospheric State, and Precipitation at Summit (ICECAPS) project. During the first 20 months in operation, this complementary suite of active and passive ground-based sensors and radiosondes has provided new and unique perspectives on important cloud–atmosphere properties. High atop the GIS, the atmosphere is extremely dry and cold with strong near-surface static stability predominating throughout the year, particularly in winter. This low-level thermodynamic structure, coupled with frequent moisture inversions, conveys the importance of advection for local cloud and precipitation formation. Cloud liquid water is observed in all months of the year, even the particularly cold and dry winter, while annual cycle observations indicate that the largest atmospheric moisture amounts, cloud water contents, and snowfall occur in summer and under southwesterly flow. Many of the basic structural properties of clouds observed at Summit, Greenland, particularly for low-level stratiform clouds, are similar to their counterparts in other Arctic regions. The ICECAPS observations and accompanying analyses will be used to improve the understanding of key cloud–atmosphere processes and the manner in which they interact with the GIS. Furthermore, they will facilitate model evaluation and development in this data-sparse but environmentally unique region.

[1] Surface-based temperature inversions (SBIs) are studied at Summit Station in central Greenland during the period spanning July 2010 to May 2012. The frequency and intensity of SBI are examined using microwave radiometer (MWR) temperature retrievals, radiosonde profiles, and near-surface meteorological data. Using the MWRs' high temporal resolution, the diurnal, monthly, and annual cycles are investigated. Monthly mean values in SBI occurrence and intensity show that surface-based inversions are prevalent in the winter with decreasing values in the summer months. A case study on 20 February 2011 suggests that factors other than solar elevation angle influence the intensity of surface-based inversions. An increase in liquid water path corresponds to a decrease in SBI intensity, suggesting that liquid-bearing clouds, especially within the lowest 1 km, are associated with weaker surface-based inversions.

Differences in cloud effective radius and cloud droplet number concentration (CDNC) estimates inferred from the Aqua MODIS Collections 5.1 and 6 cloud products (MYD06) are examined for warm clouds over global oceans for the year 2008. Individual pixel level retrievals for both collections are aggregated to 1°×1° and compared globally and regionally for the three main spectral channel pairs used for MODIS cloud optical property retrievals. Comparisons between both collections are performed for cases in which all channel pairs are successfully retrieved for each pixel and for cases where one or more channel pairs are successful. The contribution to the observed differences of several key MYD06 Collection 6 algorithm updates are also explored. Global results show a neutral to positive (> 50 cm−3) change for C6-derived CDNC relative to C5.1 for the 1.6 and 2.1 μm channel retrievals corresponding to a neutral to −2 μm difference in droplet effective radius. For 3.7 μm retrievals, CDNC results show a negative change in the tropics, with differences transitioning towards positive values with increasing latitude spanning (−25 to +50 cm−3) related to a (+2.5 to −1 μm) transition in effective radius. Regionally, the magnitude and behavior of the annual CDNC cycle are compared for each retrieval channel pair. Results from this study indicate significant intercollection differences in aggregated values of microphysical parameters which may have implications for existing MODIS derived climatologies and validation studies.