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NASA NTRS2020

Application of quasi-deep convective clouds method for MODIS and VIIRS TEB calibration assessments

A technique to use deep convective clouds (DCC) and quasi-DCC (qDCC) for the calibration assessment of the thermal emissive bands (TEB) on remote sensing instruments has proven feasible. The Terra and Aqua MODIS and S-NPP and NOAA-20 VIIRS TEB calibration uses a nonlinear algorithm whose nonlinear coefficients rely on on-orbit black body (BB) warm-up and cool-down (WUCD) activities for updates. However, the limited BB temperature range affects the calibration’s uncertainty. The DCC core, one of the coldest Earth scenes, is suitable for MODIS calibration assessments; more specifically, for the evaluation of the offset effect in its TEB quadratic calibration function. Moreover, nighttime qDCC measurements provide the advantage of removing solar reflectance effects, thus enhancing the assessment’s accuracy for the midwave infrared TEB. In this paper, the qDCC method is applied to the Terra MODIS and VIIRS TEB. Their stabilities are assessed using long-term DCC and qDCC trending measurements over the instruments’ entire missions. The measurements from bands with an approximately 11-μm wavelength are used to identify the DCC pixels. MODIS band31 (~ 11m) has demonstrated stable performance and accurate calibration for both instruments throughout their respective missions. MODIS band31 can therefore be used as a reference for the other TEB. Furthermore, it also allows for a Terra and Aqua MODIS TEB cross-comparison. The assessment results, along with the calibration uncertainty and Level 1B product impact modeling, can be quite helpful for calibration improvements.

MODIS↗

NASA NTRS2008

Frequency of Deep Convective Clouds and Global Warming

This slide presentation reviews the effect of global warming on the formation of Deep Convective Clouds (DCC). It concludes that nature responds to global warming with an increase in strong convective activity. The frequency of DCC increases with global warming at the rate of 6%/decade. The increased frequency of DCC with global warming alone increases precipitation by 1.7%/decade. It compares the state of the art climate models' response to global warming, and concludes that the parametrization of climate models need to be tuned to more closely emulate the way nature responds to global warming.

infrared↗

DOE OSTI2023

An Intercomparison of Large‐Eddy Simulations of a Convection Cloud Chamber Using Haze‐Capable Bin and Lagrangian Cloud Microphysics Schemes

Abstract Recent in situ observations show that haze particles exist in a convection cloud chamber. The microphysics schemes previously used for large‐eddy simulations of the cloud chamber could not fully resolve haze particles and the associated processes, including their activation and deactivation. Specifically, cloud droplet activation was modeled based on Twomey‐type parameterizations, wherein cloud droplets were formed when a critical supersaturation for the available cloud condensation nuclei (CCN) was exceeded and haze particles were not explicitly resolved. Here, we develop and adapt haze‐capable bin and Lagrangian microphysics schemes to properly resolve the activation and deactivation processes. Results are compared with the Twomey‐type CCN‐based bin microphysics scheme in which haze particles are not fully resolved. We find that results from the haze‐capable bin microphysics scheme agree well with those from the Lagrangian microphysics scheme. However, both schemes significantly differ from those from a CCN‐based bin microphysics scheme unless CCN recycling is considered. Haze particles from the recycling of deactivated cloud droplets can strongly enhance cloud droplet number concentration due to a positive feedback in haze‐cloud interactions in the cloud chamber. Haze particle size distributions are more realistic when considering solute and curvature effects that enable representing the complete physics of the activation process. Our study suggests that haze particles and their interactions with cloud droplets may have a strong impact on cloud properties when supersaturation fluctuations are comparable to mean supersaturation, as is the case in the cloud chamber and likely is the case in the atmosphere, especially in polluted conditions.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2026

Evaluating the Collision‐Coalescence Process in Idealized Cloud Convection Using Large‐Eddy Simulations With Lagrangian Microphysics

Drizzle initiation through the collision and coalescence of cloud droplets plays a crucial role in warm cloud precipitation. Recent theoretical studies suggest that the influence of collisional growth on the droplet size distribution can be quantified by a non-dimensional drizzle number (Dz). Here, large-eddy simulations with Lagrangian microphysics are employed to evaluate the theory by simulating a tall convection-cloud chamber under various conditions. Results show that the smaller the Dz, the larger the impact of collisions on the right tail of the droplet size distribution, consistent with the theory. The simulations confirm that the collision rate can be estimated from the droplet size distribution interacting only with cloud droplets of the same size at the mode radius. This suggests that the idealized theory can be a useful tool to design a cloud chamber for drizzle investigation, as well as to represent drizzle formation in models of real atmospheric clouds.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2017

A Method for Obtaining High Frequency, Global, IR-Based Convective Cloud Tops for Studies of the TTL

Models of varying complexity that simulate water vapor and clouds in the Tropical Tropopause Layer (TTL) show that including convection directly is essential to properly simulating the water vapor and cloud distribution. In boreal winter, for example, simulations without convection yield a water vapor distribution that is too uniform with longitude, as well as minimal cloud distributions. Two things are important for convective simulations. First, it is important to get the convective cloud top potential temperature correctly, since unrealistically high values (reaching above the cold point tropopause too frequently) will cause excessive hydration of the stratosphere. Second, one must capture the time variation as well, since hydration by convection depends on the local relative humidity (temperature), which has substantial variation on synoptic time scales in the TTL. This paper describes a method for obtaining high frequency (3-hourly) global convective cloud top distributions which can be used in trajectory models. The method uses rainfall thresholds, standard IR brightness temperatures, meteorological temperature analyses, and physically realistic and documented corrections IR brightness temperature corrections to derive cloud top altitudes and potential temperatures. The cloud top altitudes compare well with combined CLOUDSAT and CALIPSO data, both in time-averaged overall vertical and horizontal distributions and in individual cases (correlations of .65-.7). An important finding is that there is significant uncertainty (nearly .5 km) in evaluating the statistical distribution of convective cloud tops even using lidar. Deep convection whose tops are in regions of high relative humidity (such as much of the TTL), will cause clouds to form above the actual convection. It is often difficult to distinguish these clouds from the actual convective cloud due to the uncertainties of evaluating ice water content from lidar measurements. Comparison with models show that calculated cloud top altitudes are generally higher than those calculated by global analyses (e.g., MERRA). Interannual variability in the distribution of convective cloud top altitudes is also investigated.

hydration↗

NASA NTRS2010

Estimating Glaciation Temperature of Deep Convective Clouds with Remote Sensing Data

Major uncertainties exist for observing and modeling ice content inside deep convective clouds (DCC). One of the difficulties has been the lack of characterization of vertical profiles of cloud hydrometeor phase. Here we propose a technique to estimate the DCC glaciation temperature using passive remote sensing data. It is based on a conceptual model of vertical hydrometeor size profiles inside DCCs. Estimates from the technique agree well with our general understanding of the problem. Furthermore, the link between vertical profiles of cloud particle size and hydrometeor thermodynamic phase is confirmed by a 3-13 cloud retrieval technique. The technique is applied to aircraft measurements of cloud side reflectance and the result was compared favorably with an independent retrieval of thermodynamic phase based on different refractive indices at 2.13 micron and 2.25 micron. Possible applications of the technique are discussed.

Yuan, Tianle↗

NASA NTRS2021

Comparisons of Cloud In-Situ Microphysical Properties of Deep Convective Clouds to Appendix D/P using Data from the HAIC-HIWC and HIWC-RADAR I Flight Campaigns

In-situ cloud data from three international flight campaigns are compared to the Federal Aviation Administration Title 14 Code of Federal Regulations Part 33 Appendix D mixed-phase/glaciated environmental envelope, and the corresponding identical European Aviation Safety Agency CS-25 Appendix P envelope. The appendices consist of a temperature-altitude envelope, a 99th percentile total water content envelope at the 17.4 Nm distance scale, a distance factor for estimation at other distance scales, ice crystal median mass diameter, and recommended liquid water content levels in mixed-phase icing conditions. The data were collected during 54 flights out of one subtropical and two tropical locations, with 472 runs from about 17,000’ to 39,000’ in approximately 115 clouds. The campaigns provide about 29,600 Nm of in situ data in deep convection over four targeted temperature intervals: -10, -30, -40, and -50, all ± 5 C. The dataset is a modern and unique documentation of the ice crystal icing environment, and results described in this article will contribute to regulatory and industry assessment of Appendices D and P.

Ice Crystal Icing↗

NASA NTRS2020

Assessment of SNPP VIIRS RSB detector-to-detector differences using deep convective clouds and deserts

An accurate on-orbit characterization of the Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Suomi National Polar-orbiting Partnership satellite is essential to satisfy the requirements from NOAA, NASA, and the general research community for high-quality operational and research products. NASA’s Land Science Investigator-led Processing System (LSIPS) sensor data records (SDRs) are utilized to assess the stability of the calibrated top of atmosphere reflectance over the deep convective clouds (DCCs) and over the Libya-4desert site. The results from DCCs and desert show detector-to-detector (D2D) reflectance differences in the VIIRS reflective solar bands, 10 moderate-resolution bands (M-bands, M1to M5, M7 to M11), and three imagery resolution bands (I-bands, I1-I3). More importantly, the D2D differences exhibit an increase in magnitude over time for bands M1 to M5 and I1to I2, which induces noticeable striping and higher uncertainties in the downstream data products. The largest D2D difference is observed in bands M1 and M2, with magnitudes greater than1.8% in trends among the 16 detectors and standard deviations less than 0.12% over time. TheD2D stability assessment results over DCCs are consistent with those observed over the Libya-4desert site. To improve the quality of the calibrated SDR reflectance data, NASA’s VIIRS characterization support team has improved the calibration algorithm to incorporate corrections based on these DCC measurements to mitigate the striping (detector differences) observed in the current version of the SDR. These improvements are planned to be included in the next mission reprocessing of the LSIPS land products.

SNPP VIIRS↗

NASA NTRS2006

AIRS Observations of Deep Convective Clouds

Large thunderstorms can be identified in the AIRS data as areas where the brightness temperature of the 1231 cm-1 atmospheric window channel in non-polar areas is less than 210 K. Each day about 6000 large thunderstorms are identified, almost exclusively within 30 degrees of the equator. Since the size of the AIRS footprint at nadir is 13.5 km, a brightness temperature of less than 210 K indicates that the top of the anvil of the thunderstorm protrudes well into the tropopause. Such objects are commonly referred to as Deep Convective Clouds (DCC). Our interest in DCC was motivated by the question 'Are severe weather events increasing due to global warming'. Each DCC is a severe weather event, although not on the scale of the much less frequent hurricanes, which can be identified in the AIRS data as clusters of several hundred DCC. The number of DCC per day has been fairly stable over the past four years for the mean of the tropical oceans, but a significant increase can be seen day and night in the Atlantic Ocean. The number of DCC per day shows a strong seasonal and latitudinal dependence, with the peak count lagging the solstice of the latitude zone by about 2 months. The most prominent features in brightness temperature spectra of DCC are due to stratospheric CO2, Ozone and Methane. In the channels with weighting functions below the stratosphere the brightness temperature is typically 205 K, with a characteristic 1 to 2.5 K drop between 1000 and 750 cm-1, equivalent to a 2-4 % drop in emissivity. This is likely due to the presence of cirrus (ice) particles. Some of this analysis of DCC can be extended using past and future operational sounders in polar orbit.

temperature↗

DOE OSTI2026

Inverse Mapping of the Collision Kernel and Wall Flux Scaling in a Tall Convection‐Cloud Chamber Using Local Sensors and Knowledge‐Informed Deep Learning

Droplet collision–coalescence is a crucial process in cloud physics, but accurately representing this process under different dynamical conditions remains challenging. A proposed future convective‐cloud chamber aims to investigate this key process, but the method for observing it remains unclear, even though it is theoretically established that collision‐coalescence will occur. This study serves as a proof‐of‐concept demonstration of how knowledge‐informed deep learning, combined with measurement data from local sensors in the chamber, can be used to estimate the collision kernels, which determine how the droplet size distribution evolves during collision‐coalescence. In addition to estimating the collision kernel, we also address wall fluxes, another uncertain but important process that acts as a source of heat and moisture in the chamber. Ensemble runs of large‐eddy simulations are conducted by scaling the wall fluxes and the collision kernel, while the measured flow and cloud properties are used as inputs for a neural network. Results indicate that this approach successfully maps the scaling of wall fluxes and the collision kernel with biases of approximately 1% or less relative to the range of the target data. This proof‐of‐concept lays the groundwork for future applications; when the real measurements are available, real sensor data combined with the trained model presented in this work will enable estimation of the actual wall fluxes and collision kernel.

cloud chamber↗

NASA NTRS2012

Simulations of Infrared Radiances Over a Deep Convective Cloud System Observed During TC4: Potential for Enhancing Nocturnal Ice Cloud Retrievals

Retrievals of ice cloud properties using infrared measurements at 3.7, 6.7, 7.3, 8.5, 10.8, and 12.0 microns can provide consistent results regardless of solar illumination, but are limited to cloud optical thicknesses tau < approx.6. This paper investigates the variations in radiances at these wavelengths over a deep convective cloud system for their potential to extend retrievals of tau and ice particle size D(sub e) to optically thick clouds. Measurements from the Moderate Resolution Imaging Spectroradiometer Airborne Simulator--ASTER, the Scanning High-resolution Interferometer Sounder, the Cloud Physics Lidar (CPL), and the Cloud Radar System (CRS) aboard the NASA ER-2 aircraft during the NASA TC4 (Tropical Composition, Cloud and Climate Coupling) experiment flight during 5 August 2007, are used to examine the retrieval capabilities of infrared radiances over optically thick ice clouds. Simulations based on coincident in-situ measurements and combined cloud tau from CRS and CPL measurements are comparable to the observations. They reveal that brightness temperatures at these bands and their differences (BTD) are sensitive to tau up to approx.20 and that for ice clouds having tau > 20, the 3.7 - 10.8 microns and 3.7 - 6.7 microns BTDs are the most sensitive to D(sub e). Satellite imagery appears consistent with these results. Keywords: clouds; optical depth; particle size; satellite; TC4; multispectral thermal infrared

Minnis, Patrick↗

NASA NTRS2012

Simulations of Infrared Radiances Over a Deep Convective Cloud System Observed During TC4- Potential for Enhancing Nocturnal Ice Cloud Retrievals

CRS and CPL↗

NASA NTRS2016

Assessment of MODIS RSB Detector Uniformity Using Deep Convective Clouds

For satellite sensor, the striping observed in images is typically associated with the relative multiple detector gain difference derived from the calibration. A method using deep convective cloud (DCC) measurements to assess the difference among detectors after calibration is proposed and demonstrated for select reflective solar bands (RSBs) of the Moderate Resolution Imaging Spectroradiometer (MODIS). Each detector of MODIS RSB is calibrated independently using a solar diffuser (SD). Although the SD is expected to accurately characterize detector response, the uncertainties associated with the SD degradation and characterization result in inadequacies in the estimation of each detector's gain. This work takes advantage of the DCC technique to assess detector uniformity and scan mirror side difference for RSB. The detector differences for Terra MODIS Collection 6 are less than 1% for bands 1, 3-5, and 18 and up to 2% for bands 6, 19, and 26. The largest difference is up to 4% for band 7. Most Aqua bands have detector differences less than 0.5% except bands 19 and 26 with up to 1.5%. Normally, large differences occur for edge detectors. The long-term trending shows seasonal oscillations in detector differences for some bands, which are correlated with the instrument temperature. The detector uniformities were evaluated for both unaggregated and aggregated detectors for MODIS band 1 and bands 3-7, and their consistencies are verified. The assessment results were validated by applying a direct correction to reflectance images. These assessments can lead to improvements to the calibration algorithm and therefore a reduction in striping observed in the calibrated imagery.

Clouds↗

NASA NTRS2016

A Method for Obtaining High Frequency, Global, IR-Based Convective Cloud Tops for Studies of the Tropical Tropopause Layer

Models of varying complexity that simulate water vapor and clouds in the Tropical Tropopause Layer (TTL) show that including convection directly is essential to properly simulating the water vapor and cloud distribution. In boreal winter, for example, simulations without convection yield a water vapor distribution that is too uniform with longitude, as well as cloud incidences that are too low. Two things are important for convective simulations. First, it is important to get the convective cloud top potential temperature correctly, since unrealistically high values (reaching above the cold point tropopause too frequently) will cause excessive hydration of the stratosphere. Second, one must capture the time variation as well, since hydration by convection depends on the local relative humidity (temperature), which has substantial variation on synoptic time scales in the TTL. This paper describes a method for obtaining high frequency (3-hourly) global convective cloud top distributions which can be used in trajectory models. The method uses rainfall thresholds, standard IR (infra-red) brightness temperatures, meteorological temperature analyses, and physically realistic and documented corrections to IR brightness temperatures to derive cloud top altitudes and potential temperatures. The cloud top altitudes compare well with combined CLOUDSAT and CALIPSO data, both in time-averaged overall vertical and horizontal distributions and in individual cases (correlations of .65-.7). Results from the method are compared to convective distributions currently used by global models. In general, the method shows that models underestimate convective cloud top altitudes.

tropopause↗

NASA NTRS1985

Infrared remote sensing of convective clouds and amount of rainfall over the Tibet Plateau area

The pattern of heavy rainfall in the Tibet Plateau area is usually preceded by a high growth rate of the convective clouds followed by a rapid collapse of the cloud top as observed on satellite infrared remote images. This study shows that the amount of rainfall can be estimated from the volumetric dissipation of clouds per unit area passing through the area of interest. This study also shows that the ratio of ground observed rainfall over the volumetric dissipation of clouds per unit area is constant for relatively heavier rainfall, and is smaller for very light rainfall. This is due to the fact that the lighter rainfall consumes higher percentage of rain drops in evaporation before they reach the ground.

Hung, R. J.↗

NASA NTRS1981

Retrieval of thermodynamic variables with deep convective clouds Experiments in three dimensions

A three-dimensional numerical cloud model has been used to test a method for retrieving temperature and pressure deviation fields from detailed wind and water fields in deep convective clouds. A comparison of the retrieved fields with the output from the numerical model was used to test the validity of the theoretical treatment and accuracy of the programming. The local time derivatives of each of the velocity components are known to be potential problem sources in using Doppler radar data, and a test was done with this derivative estimated over a 4 min time span rather than 30 s, resulting in excellent agreement with the original solution for this data set. When the local derivative was eliminated, the solution was judged useful for general temperature patterns. Errors due to the inability to measure cloudwater mixing ratio and inaccuracies in rainwater mixing ratio were found to be significant, but not so severe as in the turbulence and steady-state sensitivity tests.

Hane, C. E.↗

NASA NTRSDate not supplied

The Impact of Pixel Size on the Characterization of Deep Convective Clouds for Calibration

The NASA CERES project provides the scientific community the observed TOA SW and LW fluxes for climate monitoring and climate model validation. CERES utilizes hourly geostationary imager derived broadband fluxes, which rely on the channel radiances and associated cloud retrievals, to estimate the broadband fluxes between CERES observations. This requires stable imager visible channel calibration, which the CERES project verifies by utilizing deep convective clouds (DCC) as an invariant Earth target. GSICS, which is an international collaboration, is also evaluating the DCC invariant target calibration methodology to provide consistent calibration coefficients across geostationary imagers anchored to the Aqua-MODIS calibration reference. Tropical DCC are the brightest, coldest, most Lambertian, top of the atmosphere Earth targets. The DCC invariant target calibration methodology relies on a large ensemble of tropical DCC-identified pixel-level reflectances, which are histogrammed to find the mode reflectance of the probability density function (PDF). The imager stability is monitored by tracking the monthly DCC PDF mode reflectance over time. Radiometric scaling is accomplished by ratioing the GEO and VIIRS DCC mode reflectance values. The PDF shape and mode dependency on sensor pixel resolution, which varies among sensors, is unknown. This study will characterize the impact of pixel resolution on the DCC PDFs by aggregating Landsat 30-m pixel reflectances into various coarser pixel resolutions ranging from 100-m to 4-km, and comparing the corresponding PDF statistics. This analysis will assist in improving the uncertainty in a DCC-based intercalibration between instruments with different pixel resolutions.

Conor Haney↗

NASA NTRSDate not supplied

The Impact of Pixel Size on the Characterization of Deep Convective Clouds for Calibration

The NASA CERES project provides the scientific community the observed TOA SW and LW fluxes for climate monitoring and climate model validation. CERES utilizes hourly geostationary imager derived broadband fluxes, which rely on the channel radiances and associated cloud retrievals, are used to estimate the broadband fluxes between CERES observations. This requires stable and consistent cross-platform imager visible channel calibration. The CERES project utilizes deep convective clouds (DCC) as an invariant Earth target to both monitor the stability of sensors and for radiometric scaling. GSICS, an international collaboration, is also evaluating and implementing the DCC invariant target calibration methodology to provide consistent calibration coefficients across geostationary imagers anchored to the AquaMODIS or the NOAA-20 VIIRS calibration reference. Tropical DCC are the brightest, coldest, most Lambertian, top of the atmosphere Earth targets. The DCC invariant target calibration methodology relies on a large ensemble of tropical D CC-identified pixel-level reflectances, which are aggregated as probability density functions (PDF). By assuming the monthly PDF shape is otherwise consistent in time excepting shifts in reflectance caused by changes in the sensor calibration, the imager stability is monitored. Radiometric scaling is accomplished by ratioing the sensor pair DCC PDF reflectance values. The success of the DCC methodology relies on consistent PDF distributions. The goal of this study is to determine the impact of pixel resolution on the DCC reflectance distribution. Single SNPP-VIIRS 750-m and Landsat 8 OLI 30-m granules are aggregated to degrade the pixel resolution from the native level. The DCC pixels are identified using a BT threshold. Most of the brightest DCC pixels are also the coldest, although there are exceptions. It was found that increasing the BT threshold exponentially increased the number of darker pixels. The pixel resolution did not seem to impact the DCC reflectance PDF distribution for pixel resolutions less than 3 km, which suggests that imagers of varying pixel resolutions may be radiometrically scaled to each other using DCC targets.

DCC↗