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The observed chemical composition of the atmosphere near the Venus cloud tops, the refractive index of cloud particles as deduced from polarization data, and the 3-4-micron reflectivity of the clouds are discussed. The data are shown to be consistent with and to strongly support the possibility that the topmost clouds of Venus are composed of concentrated aqueous HCl solutions.
This dataset contains daily files with thermodynamic profiles retrieved with the optimal estimation physical retrieval TROPoe (Turner and Löhnert 2014; Turner and Blumberg 2019; Turner and Löhnert 2021). The profiles are retrieved every 10 minutes from instantaneous observations from the NREL ASSIST-II (SN 11) infrared spectrometer. Observations are noise-filtered but not averaged in time to minimize errors due to non-uniform clouds. Additional input data in TROPoe are cloud base height from a collocated NREL Upgraded Galion lidar #1212-60 and temperature, RH, and pressure from collocated PNNL met station. The lidar beta is used to estimate the cloud base height through a modified algorithm by Newsom et al., 2019 used at ARM SGP. The full pipeline for running the retrieval is available at https://github.com/StefanoWind/TROPoe_processor. In addition to these temporally resolved input data, TROPoe requires an a priori dataset (prior) which provides mean climatological estimates of thermodynamic profiles and specifies how temperature and humidity covary with height as an input (for details see e.g. Djalalova et al. 2022). The prior is a key component of the retrieval and provides a constraint on the ill-posed inversion problem. A monthly prior was computed from operational radiosonde launches at Upton, NY.
Ka-Band Scanning ARM Cloud Radar (KASACR); Boundary Layer RHI Scan, auxiliary data
Ka-Band Scanning ARM Cloud Radar (KASACR); Corner reflector, RHI Scan, auxiliary data
Ka-Band Scanning ARM Cloud Radar (KASACR); Fixed-Pointing Mode, auxiliary data
Ka-Band Scanning ARM Cloud Radar (KASACR); Hemispherical Sky RHI Scan, auxiliary data
Ka-Band Scanning ARM Cloud Radar (KASACR); Range Height Indicator Scan, auxiliary data
Ka-Band Scanning ARM Cloud Radar (KASACR); Vertically-Pointing Scan, auxiliary data
Ka-Band Scanning ARM Cloud Radar (KASACR); Zenith Pointing PPI Scan, auxiliary data
Ka-Band Scanning ARM Cloud Radar (KASACR); Zenith Centered RHI Scan, auxiliary data
In this paper we examine differences between cloud pressures retrieved from the Ozone Monitoring Instrument (OMI) using the ultraviolet rotational Raman scattering (RRS) algorithm and those from the thermal infrared (IR) Aqua/MODIS. Several cloud data sets are currently being used in OMI trace gas retrieval algorithms including climatologies based on IR measurements and simultaneous cloud parameters derived from OMI. From a validation perspective, it is important to understand the OMI retrieved cloud parameters and how they differ with those derived from the IR. To this end, we perform radiative transfer calculations to simulate the effects of different geophysical conditions on the OMI RRS cloud pressure retrievals. We also quantify errors related to the use of the Mixed Lambert-Equivalent Reflectivity (MLER) concept as currently implemented of the OMI algorithms. Using properties from the Cloudsat radar and MODIS, we show that radiative transfer calculations support the following: (1) The MLER model is adequate for single-layer optically thick, geometrically thin clouds, but can produce significant errors in estimated cloud pressure for optically thin clouds. (2) In a two-layer cloud, the RRS algorithm may retrieve a cloud pressure that is either between the two cloud decks or even beneath the top of the lower cloud deck because of scattering between the cloud layers; the retrieved pressure depends upon the viewing geometry and the optical depth of the upper cloud deck. (3) Absorbing aerosol in and above a cloud can produce significant errors in the retrieved cloud pressure. (4) The retrieved RRS effective pressure for a deep convective cloud will be significantly higher than the physical cloud top pressure derived with thermal IR.
Evidence is presented for a sequential development of cloud water, rainwater, graupel, and crystalline ice with the aging of the cloud. This evidence is based on in-cloud microphysical data set collected in Florida convective towers that were penetrated close to their tops near -10 C; the very rapid onset of graupel that appears on repeat penetrations of some towers is of particular interest. A separate data set shows a large scatter in the relationship between the maximum value of cloud water and vertical velocity which indicates that measurements of cloud water can be misleading as an indication of growth activity. The sequential pass data showing the evolution of ice and water are consistent with a rime-splintering, secondary ice production hypothesis.
The application to rainfall prediction of cloud liquid water data obtained from the SCAMS experiment of Nimbus-6 is explored. The study area is the Pacific Northwest coast of the United States, where rainfall is produced by extratropical storms that approach from across the Pacific Ocean. The SCAMS data related to cloud liquid water over the ocean, and coastal rainfall data, are analyzed for 20 different storm systems in the northeastern Pacific Ocean; these produced significant rainfall from Washington to central California during the period October 1975 through March 1976. Results show that the distribution of storm cloud water analyzed from the SCAMS data over the ocean foreshadows the distribution of coastal rainfall accumulated from the storm at a later time. It is concluded that passive microwave sensor measurements of cloud water over the ocean, when used in conjunction with numerical and other objective guidance, can be used to enhance the accuracy of predictions of coastal rainfall distribution. Limitations in the SCAMS measurements and in the data analysis and interpretation are noted.
This dataset contains daily files with thermodynamic profiles retrieved with the optimal estimation physical retrieval TROPoe v0.19 (Turner and Löhnert, 2014; Turner and Blumberg, 2019; Turner and Löhnert, 2021). The profiles are retrieved every 10 minutes from instantaneous observations from the NREL ASSIST-II (SN 12) infrared spectrometer. Observations are noise-filtered but not averaged in time to minimize errors due to non-uniform clouds. Additional input data in TROPoe are cloud base height from a collocated NREL Vaisala CL51 ceilometer (when available) or vertically staring Halo XR lidar, and surface temperature, relative humidity, and pressure from the collocated Oregon State University met tower. The full pipeline to run the retrieval is available at https://github.com/StefanoWind/TROPoe_processor. In addition to these temporally resolved input data, TROPoe requires an a priori dataset (prior) that provides mean climatological estimates of thermodynamic profiles and specifies how temperature and humidity covary with height as an input (for details see, e.g., Djalalova et al., 2022). The prior is a key component of the retrieval and provides a constraint on the ill-posed inversion problem. A monthly prior was computed from operational radiosonde launches at Upton, NY.
A hybrid bispectral threshold method (HBTM) is presently used to compare cloud amounts derived from Landsat digital data over 22 regions having various cloud types with cloudiness information obtained from collocated, nearly-simultaneous 4 x 8-km GOES visible and IR data. A sensitivity analysis indicates that an rms underestimation of about 0.01 in clear sky reflectance by the HBTM increased the GOES cloud amount by 0.06, which is more than twice the decrese in cloud amount obtained by an equivalent increase in clear-sky reflectance. Landsat imagery and cloud properties derived from the Landsat data are used to explain how the partially cloud-filled GOES pixels were treated by the HBTM. It is found that the HBTM accounts for the effects of partially cloud-filled FOVs in most cases of the present study.
Each NASA Distributed Active Archive Center (DAAC) faces the challenge of dealing with an increasingly diverse number of publishable data products from diverse data producers. Data producers, on the other hand, may experience pain points when interacting with the EOSDIS for the first time or when publishing different data at different DAACs. As a result, there has been a growing need to develop a common software framework that serves as a common interface for data producers, rigorously defines the data publication procedure for DAAC staff, facilitates the management of various data publication processes, and tracks the progress of data publication. This software should also account for the different configurations at different DAACs. Currently, two primary data publication workflow and tracking tools exist in operation at the EOSDIS: Semi-Automated ingest System (SAuS) and Data Publication workflow Portal (DAPPeR). However, neither tool is cloud-ready. Automated data processing could be managed by Cumulus, an EOSDIS cloud-based data ingest, archive and management system. However, Cumulus does not support manual tasks or on-premise implementations. We propose to develop the Earthdata Publication Minimum Viable Product (Earthdata Pub MVP) -- a cloud-hosted solution that works with both cloud and on-premise systems and implements the communications and exchange requirements generated by the Earthdata Pub information architecture team.
The interpretation of surface and aircraft measurements of cloud properties taken during field programs must take into account the large-scale cloud and meteorological conditions. Cloud properties are also required at scales beyond the point and line data taken from ground and aircraft platforms. Satellite data can provide a quantitative description of these large-scale cloud properties. When derived from geostationary satellite data, the cloud fields constitute a unique source for evaluating the development and demise of a cloud system. Satellites, however, can only see the tops of clouds, so that cloud layers below the uppermost cloud deck may remain undetected resulting in a incomplete depiction of the cloud system. Some multilayer clouds are amenable to detection from satellites. Many, especially in midlatitude cyclonic systems, can only be observed from the surface. A combination of surface and satellite cloud observations should be the most complete quantification of large-scale cloudiness if there are sufficient surface measurements. During the First International Satellite Cloud Climatology Project (ISCCP) Regional Experiment Phase 2 (FIRE-2) Cirrus Intensive Field Observation (IFO) period (November 13 - December 7, 1991) conducted at Coffeyville, Kansas, cirrus observations were taken in a variety of conditions. The IFO area was selected for a variety of reasons including the relatively dense network of surface weather stations and special surface instrumentation sites. Thus, the FIRE-2 IFO presents an excellent opportunity to combine cloud observations from surface and satellite observations. This paper presents an analysis of cloud properties on a mesoscale grid using satellite cloud property retrievals, surface observer data, and rawinsonde temperature and humidity profiles.
The authors analyze the influence of Sea Surface Temperature (SST) and surface wind divergence on atmospheric thermodynamic structure and the resulting effects on the occurrence of deep convection using National Meteorological Center radiosonde data and International Satellite Cloud Climatology Program data for July 1983-July 1985. The onset of deep convection requires not only the existence of positive convective available potential energy (CAPE), but also an unstable planetary boundary layer (PBL). A stable PBL is observed to suppress deep convection even when CAPE is positive. Variations of SST have a major effect on CAPE, but surface wind divergence can also affect deep convection by changing the lapse rate in the lower troposphere and humidity in the PBL. Specifically, when SST is greater than or equal to 28 C, CAPE is always positive, and surface wind divergence does not qualitatively change the buoyancy profile above the PBL. Strong surface wind divergence, however, stabilizes the PBL so as to suppress the initiation of deep convection. In warm SST regions, CAPE is greater than 0 regardless of assumptions about condensate loading, although the pseudoadiabatic limit is more consistent with the observed deep convection than the reversible moist-adiabatic limit under these circumstances. When SST is less than 27 C, CAPE is usually negative and inhibits convection, but strong surface wind convergence can destabilize the inversion layer and moisten the PBL enough to make the atmosphere neutrally stable in the mean. As a result, deep convection is generally enhanced either when SST is greater than or equal to 28 C in the absence of strong surface wind divergence or when strong surface wind convergence occurs even if SST is less than 27 C. The anomalous suppression of deep convection in the warm area of the equatorial west Pacific lying between the intertropical convergence zone (ITCZ) and south Pacific convergence zone (SPCZ) is probably caused by dryness in the PBL and an inversion in that area. The seasonal cycles of deep convection and surface wind divergence are in phase with the maximum solar radiation and lead SST for one to three months in the central Pacific. The change of PBL relative humidity plays a critical role in the changeover to convective instability in this case. The seasonal change of deep convection and associated clouds seems not to have important effects on the seasonal change of local SST in the central Pacific.