Ten Years of Cloud Properties from MODIS: Global Statistics and Use in Climate Model Evaluation

The NASA Moderate Resolution Imaging Spectroradiometer (MODIS), launched onboard the Terra and Aqua spacecrafts, began Earth observations on February 24, 2000 and June 24,2002, respectively. Among the algorithms developed and applied to this sensor, a suite of cloud products includes cloud masking/detection, cloud-top properties (temperature, pressure), and optical properties (optical thickness, effective particle radius, water path, and thermodynamic phase). All cloud algorithms underwent numerous changes and enhancements between for the latest Collection 5 production version; this process continues with the current Collection 6 development. We will show example MODIS Collection 5 cloud climatologies derived from global spatial . and temporal aggregations provided in the archived gridded Level-3 MODIS atmosphere team product (product names MOD08 and MYD08 for MODIS Terra and Aqua, respectively). Data sets in this Level-3 product include scalar statistics as well as 1- and 2-D histograms of many cloud properties, allowing for higher order information and correlation studies. In addition to these statistics, we will show trends and statistical significance in annual and seasonal means for a variety of the MODIS cloud properties, as well as the time required for detection given assumed trends. To assist in climate model evaluation, we have developed a MODIS cloud simulator with an accompanying netCDF file containing subsetted monthly Level-3 statistical data sets that correspond to the simulator output. Correlations of cloud properties with ENSO offer the potential to evaluate model cloud sensitivity; initial results will be discussed

Laboratory for Atmospheres Instrument Systems Report

Studies of the atmospheres of our solar system's planets including our own require a comprehensive set of observations, relying on instruments on spacecraft, aircraft, balloons, and on the surface. These instrument systems perform one or both of the following: 1) provide information leading to a basic understanding of the relationship between atmospheric systems and processes, and 2) serve as calibration references for satellite instrument validation. Laboratory personnel define requirements, conceive concepts, and develop instrument systems for spaceflight missions, and for balloon, aircraft, and ground-based observations. Balloon and airborne platforms facilitate regional measurements of precipitation, cloud systems, and ozone from high-altitude vantage points, but still within the atmosphere. Such platforms serve as stepping-stones in the development of space instruments. Satellites provide nearly global coverage of the Earth with spatial resolutions and repetition rates that vary from system to system. The products of atmospheric remote sensing are invaluable for research associated with water vapor, ozone, trace gases, aerosol particles, clouds, precipitation, and the radiative and dynamic processes that affect the climate of the Earth. These parameters also provide the basic information needed to develop models of global atmospheric processes and weather and climate prediction. Laboratory scientists also participate in the design of data processing algorithms, calibration techniques, and the data processing systems

Atmospheric Instrument Systems and Technology in the Goddard Earth Sciences Division

Studies of the Earths atmosphere require a comprehensive set of observations that rely on instruments flown on spacecraft, aircraft, and balloons as well as those deployed on the surface. Within NASAs Goddard Space Flight Center (GSFC) Earth Sciences Division-Atmospheres, laboratories and offices maintain an active program of instrument system development and observational studies that provide: 1) information leading to a basic understanding of atmospheric processes and their relationships with the Earths climate system, 2) prototypes for future flight instruments, 3) instruments to serve as calibration references for satellite missions, and 4) instruments for future field validation campaigns that support ongoing space missions. Our scientists participate in all aspects of instrument activity, including component and system design, calibration techniques, retrieval algorithm development, and data processing systems. The Atmospheres Program has well-equipped labs and test equipment to support the development and testing of instrument systems, such as a radiometric calibration and development facility to support the calibration of ultraviolet and visible (UV/VIS), space-borne solar backscatter instruments. This document summarizes the features and characteristics of 46 instrument systems that currently exist or are under development. The report is organized according to active, passive, or in situ remote sensing across the electromagnetic spectrum. Most of the systems are considered operational in that they have demonstrated performance in the field and are capable of being deployed on relatively short notice. Other systems are under study or of low technical readiness level (TRL). The systems described herein are designed mainly for surface or airborne platforms. However, two Cubesat systems also have been developed through collaborative efforts. The Solar Disk Sextant (SDS) is the single balloon-borne instrument. The lidar systems described herein are designed to retrieve clouds, aerosols, methane, water vapor pressure, temperature, and winds. Most of the lasers operate at some wavelength combination of 355, 532, and 1064 nm. The various systems provide high sensitivity measurements based on returns from backscatter or Raman scattering including intensity and polarization. Measurements of the frequency (Doppler) shift of light scattered from various atmospheric constitutes can also be made. Microwave sensors consist of both active (radar) and passive (radiometer) systems. These systems are important for studying processes involving water in various forms. The dielectric properties of water affect microwave brightness temperatures, which are used to retrieve atmospheric parameters such as rainfall rate and other key elements of the hydrological cycle. Atmosphere radar systems operate in the range from 9.6 GHz to 94 GHz and have measurement accuracies from -5 to 1 dBZ; radiometers operate in the 50 GHz to 874 GHz range with accuracies from 0.5 to 1 degree K; conical and cross-track scan modes are used. Our passive optical sensors, consisting of radiometers and spectrometers, collectively operate from the UV into the infrared. These systems measure energy fluxes and atmospheric parameters such as trace gases, aerosols, cloud properties, or altitude profiles of various species. Imager spatial resolution varies from 37 m to 400 m depending on altitude; spectral resolution is as small as 0.5 nm. Many of the airborne systems have been developed to fly on multiple aircraft

Observed Differences in Spectral Microphysical Retrievals from MODIS

The microphysical structure of clouds is of fundamental importance for understanding a variety of cloud radiation and physical processes. With the advent of MODIS (Moderate Resolution Imaging Spectroradiometer) on the NASA EOS Terra and Aqua platforms, simultaneous global/daily 1km retrievals of cloud effective particle size are available using the heritage 3.7 an band from AVHRR as well as the 1.6 and 2.1 m shortwave IR bands. The MODIS cloud product (MOD06/MYD06 for MODIS Terra and Aqua, respectively) provides separate effective radii results using each of these spectral bands. It has been found that significant differences can occur between the three size retrievals, mainly for liquid water marine boundary layer clouds and especially in broken (low cloud fraction) regimes. In particular, for such regimes, effective radii derived from the MODIS 2.1 lim band can be substantially larger than retrievals from the Heritage 3.7 lam band. In this paper, we present global and regional results of the differences, including correlations, view angle dependencies, and algorithm sensitivities for the existing MODIS Collection 5 product

Reconciling Simulated and Observed Views of Clouds: MODIS, ISCCP, and the Limits of Instrument Simulators in Climate Models

The properties of clouds that may be observed by satellite instruments, such as optical depth and cloud top pressure, are only loosely related to the way clouds are represented in models of the atmosphere. One way to bridge this gap is through "instrument simulators," diagnostic tools that map the model representation to synthetic observations so that differences between simulator output and observations can be interpreted unambiguously as model error. But simulators may themselves be restricted by limited information available from the host model or by internal assumptions. This work examines the extent to which instrument simulators are able to capture essential differences between MODIS and ISCCP, two similar but independent estimates of cloud properties. We focus on the stark differences between MODIS and ISCCP observations of total cloudiness and the distribution of cloud optical thickness can be traced to different approaches to marginal pixels, which MODIS excludes and ISCCP treats as homogeneous. These pixels, which likely contain broken clouds, cover about 15% of the planet and contain almost all of the optically thinnest clouds observed by either instrument. Instrument simulators can not reproduce these differences because the host model does not consider unresolved spatial scales and so can not produce broken pixels. Nonetheless, MODIS and ISCCP observation are consistent for all but the optically-thinnest clouds, and models can be robustly evaluated using instrument simulators by excluding ambiguous observations

Equivalent Sensor Radiance Generation and Remote Sensing from Model Parameters

In this paper we describe a general procedure for calculating equivalent sensor radiances from variables output from a global atmospheric forecast model. In order to take proper account of the discrepancies between model resolution and sensor footprint the algorithm takes explicit account of the model subgrid variability, in particular its description of the probably density function of total water (vapor and cloud condensate.) The equivalent sensor radiances are then substituted into an operational remote sensing algorithm processing chain to produce a variety of remote sensing products that would normally be produced from actual sensor output. This output can then be used for a wide variety of purposes such as model parameter verification, remote sensing algorithm validation, testing of new retrieval methods and future sensor studies. We show a specific implementation using the GEOS-5 model, the MODIS instrument and the MODIS Adaptive Processing System (MODAPS) Data Collection 5.1 operational remote sensing cloud algorithm processing chain (including the cloud mask, cloud top properties and cloud optical and microphysical properties products.) We focus on clouds and cloud/aerosol interactions, because they are very important to model development and improvement

Spectral Dependence of MODIS Cloud Droplet Effective Radius Retrievals for Marine Boundary Layer Clouds

Low-level warm marine boundary layer (MBL) clouds cover large regions of Earth's surface. They have a significant role in Earth's radiative energy balance and hydrological cycle. Despite the fundamental role of low-level warm water clouds in climate, our understanding of these clouds is still limited. In particular, connections between their properties (e.g. cloud fraction, cloud water path, and cloud droplet size) and environmental factors such as aerosol loading and meteorological conditions continue to be uncertain or unknown. Modeling these clouds in climate models remains a challenging problem. As a result, the influence of aerosols on these clouds in the past and future, and the potential impacts of these clouds on global warming remain open questions leading to substantial uncertainty in climate projections. To improve our understanding of these clouds, we need continuous observations of cloud properties on both a global scale and over a long enough timescale for climate studies. At present, satellite-based remote sensing is the only means of providing such observations

The Influence of Thermodynamic Phase on the Retrieval of Mixed-Phase Cloud Microphysical and Optical Properties in the Visible and Near Infrared Region

Cloud microphysical and optical properties are inferred from the bidirectional reflectances simulated for a single-layered cloud consisting of an external mixture of ice particles and liquid droplets. The reflectances are calculated with a rigorous discrete ordinates radiative transfer model and are functions of the cloud effective particle size, the cloud optical thickness, and the values of the ice fraction in the cloud (i.e., the ratio of ice water content to total water content). In the present light scattering and radiative transfer simulations, the ice fraction is assumed to be vertically homogeneous; the habit (shape) percentage as a function of ice particle size is consistent with that used for the Moderate Resolution Imaging Spectroradiometer (MODIS) operational (Collection 4 and earlier) cloud products; and the surface is assumed to be Lambertian with an albedo of 0.03. Furthermore, error analyses pertaining to the inference of the effective particle sizes and optical thicknesses of mixed-phase clouds are performed. Errors are calculated with respect to the assumption of a cloud containing solely liquid or ice phase particles. The analyses suggest that the effective particle size inferred for a mixed-phase cloud can be underestimated (or overestimated) if pure liquid phase (or pure ice phase) is assumed for the cloud, whereas the corresponding cloud optical thickness can be overestimated (or underestimated)

Resolving ice cloud optical thickness biases between CALIOP and MODIS using infrared retrievals

Despite its importance as one of the key radiative properties that determines the impact of upper tropospheric clouds on the radiation balance, ice cloud optical thickness (IOT) has proven to be one of the more challenging properties to retrieve from space-based remote sensing measurements. In particular, optically thin upper tropospheric ice clouds (cirrus) have been especially challenging due to their tenuous nature, extensive spatial scales, and complex particle shapes and light-scattering characteristics. The lack of independent validation motivates the investigation presented in this paper, wherein systematic biases between MODIS Collection 5 (C5) and CALIOP Version 3 (V3) unconstrained retrievals of tenuous IOT (&lt; 3) are examined using a month of collocated A-Train observations. An initial comparison revealed a factor of 2 bias between the MODIS and CALIOP IOT retrievals. This bias is investigated using an infrared (IR) radiative closure approach that compares both products with MODIS IR cirrus retrievals developed for this assessment. The analysis finds that both the MODIS C5 and the unconstrained CALIOP V3 retrievals are biased (high and low, respectively) relative to the IR IOT retrievals. Based on this finding, the MODIS and CALIOP algorithms are investigated with the goal of explaining and minimizing the biases relative to the IR. For MODIS we find that the assumed ice single-scattering properties used for the C5 retrievals are not consistent with the mean IR COT distribution. The C5 ice scattering database results in the asymmetry parameter (<i>g</i>) varying as a function of effective radius with mean values that are too large. The MODIS retrievals have been brought into agreement with the IR by adopting a new ice scattering model for Collection 6 (C6) consisting of a modified gamma distribution comprised of a single habit (severely roughened aggregated columns); the C6 ice cloud optical property models have a constant <i>g</i> ≈ 0.75 in the mid-visible spectrum, 5–15 % smaller than C5. For CALIOP, the assumed lidar ratio for unconstrained retrievals is fixed at 25 sr for the V3 data products. This value is found to be inconsistent with the constrained (predominantly nighttime) CALIOP retrievals. An experimental data set was produced using a modified lidar ratio of 32 sr for the unconstrained retrievals (an increase of 28 %), selected to provide consistency with the constrained V3 results. These modifications greatly improve the agreement with the IR and provide consistency between the MODIS and CALIOP products. Based on these results the recently released MODIS C6 optical products use the single-habit distribution given above, while the upcoming CALIOP V4 unconstrained algorithm will use higher lidar ratios for unconstrained retrievals

Contemplating Synergistic Algorithms for the NASA ACE Mission

ACE is a proposed Tier 2 NASA Decadal Survey mission that will focus on clouds, aerosols, and precipitation as well as ocean ecosystems. The primary objective of the clouds component of this mission is to advance our ability to predict changes to the Earth's hydrological cycle and energy balance in response to climate forcings by generating observational constraints on future science questions, especially those associated with the effects of aerosol on clouds and precipitation. ACE will continue and extend the measurement heritage that began with the A-Train and that will continue through Earthcare. ACE planning efforts have identified several data streams that can contribute significantly to characterizing the properties of clouds and precipitation and the physical processes that force these properties. These include dual frequency Doppler radar, high spectral resolution lidar, polarimetric visible imagers, passive microwave and submillimeter wave radiometry. While all these data streams are technologically feasible, their total cost is substantial and likely prohibitive. It is, therefore, necessary to critically evaluate their contributions to the ACE science goals. We have begun developing algorithms to explore this trade space. Specifically, we will describe our early exploratory algorithms that take as input the set of potential ACE-like data streams and evaluate critically to what extent each data stream influences the error in a specific cloud quantity retrieval