Maximum likelihood estimation of cloud height from multi-angle satellite imagery

We develop a new estimation technique for recovering depth-of-field from multiple stereo images. Depth-of-field is estimated by determining the shift in image location resulting from different camera viewpoints. When this shift is not divisible by pixel width, the multiple stereo images can be combined to form a super-resolution image. By modeling this super-resolution image as a realization of a random field, one can view the recovery of depth as a likelihood estimation problem. We apply these modeling techniques to the recovery of cloud height from multiple viewing angles provided by the MISR instrument on the Terra Satellite. Our efforts are focused on a two layer cloud ensemble where both layers are relatively planar, the bottom layer is optically thick and textured, and the top layer is optically thin. Our results demonstrate that with relative ease, we get comparable estimates to the M2 stereo matcher which is the same algorithm used in the current MISR standard product (details can be found in [IEEE Transactions on Geoscience and Remote Sensing 40 (2002) 1547--1559]). Moreover, our techniques provide the possibility of modeling all of the MISR data in a unified way for cloud height estimation. Research is underway to extend this framework for fast, quality global estimates of cloud height.Comment: Published in at http://dx.doi.org/10.1214/09-AOAS243 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Time correlations and 1/f behavior in backscattering radar reflectivity measurements from cirrus cloud ice fluctuations

The state of the atmosphere is governed by the classical laws of fluid motion and exhibits correlations in various spatial and temporal scales. These correlations are crucial to understand the short and long term trends in climate. Cirrus clouds are important ingredients of the atmospheric boundary layer. To improve future parameterization of cirrus clouds in climate models, it is important to understand the cloud properties and how they change within the cloud. We study correlations in the fluctuations of radar signals obtained at isodepths of winter and fall cirrus clouds. In particular we focus on three quantities: (i) the backscattering cross-section, (ii) the Doppler velocity and (iii) the Doppler spectral width. They correspond to the physical coefficients used in Navier Stokes equations to describe flows, i.e. bulk modulus, viscosity, and thermal conductivity. In all cases we find that power-law time correlations exist with a crossover between regimes at about 3 to 5 min. We also find that different type of correlations, including 1/f behavior, characterize the top and the bottom layers and the bulk of the clouds. The underlying mechanisms for such correlations are suggested to originate in ice nucleation and crystal growth processes.Comment: 33 pages, 9 figures; to appear in the Journal of Geophysical Research - Atmosphere

Physically Explainable Deep Learning for Convective Initiation Nowcasting Using GOES-16 Satellite Observations

Convection initiation (CI) nowcasting remains a challenging problem for both numerical weather prediction models and existing nowcasting algorithms. In this study, object-based probabilistic deep learning models are developed to predict CI based on multichannel infrared GOES-R satellite observations. The data come from patches surrounding potential CI events identified in Multi-Radar Multi-Sensor Doppler weather radar products over the Great Plains region from June and July 2020 and June 2021. An objective radar-based approach is used to identify these events. The deep learning models significantly outperform the classical logistic model at lead times up to 1 hour, especially on the false alarm ratio. Through case studies, the deep learning model exhibits the dependence on the characteristics of clouds and moisture at multiple levels. Model explanation further reveals the model's decision-making process with different baselines. The explanation results highlight the importance of moisture and cloud features at different levels depending on the choice of baseline. Our study demonstrates the advantage of using different baselines in further understanding model behavior and gaining scientific insights

Black Carbon Concentration from Worldwide Aerosol Robotic Network (AERONET) Measurements

The carbon emissions inventories used to initialize transport models and general circulation models are highly parameterized, and created on the basis of multiple sparse datasets (such as fuel use inventories and emission factors). The resulting inventories are uncertain by at least a factor of 2, and this uncertainty is carried forward to the model output. [Bond et al., 1998, Bond et al., 2004, Cooke et al., 1999, Streets et al., 2001] Worldwide black carbon concentration measurements are needed to assess the efficacy of the carbon emissions inventory and transport model output on a continuous basis

Cloud boundaries during FIRE 2

To our knowledge, previous observations of cloud boundaries have been limited to studies of cloud bases with ceilometers, cloud tops with satellites, and intermittent reports by aircraft pilots. Comprehensive studies that simultaneously record information of cloud top and cloud base, especially in multiple layer cases, have been difficult, and require the use of active remote sensors with range-gated information. In this study, we examined a 4-week period during which the NOAA Wave Propagation Laboratory (WPL) 8-mm radar and the Pennsylvania State University (PSU) 3-mm radar operated quasi-continuously, side by side. By quasi-continuously, we mean that both radars operated during all periods when cloud was present, during both daytime and nighttime hours. Using this data, we develop a summary of cloud boundaries for the month of November for a single location in the mid-continental United States

Initial evaluation of profiles of temperature, water vapor, and cloud liquid water from a new microwave profiling radiometer.

To measure the vertical profiles of temperature and water vapor that are essential for modeling atmospheric processes, the Atmospheric Radiation Measurement (ARM) Program of the U. S. Department of Energy launches approximately 2600 radiosondes each year from its Southern Great Plains (SGP) facilities in Oklahoma and Kansas, USA. The annual cost of this effort exceeds $500,000 in materials and labor. Despite the expense, these soundings have a coarse temporal resolution and reporting interval compared with model time steps. In contrast, the radiation measurements used for model evaluations have temporal resolutions and reporting intervals of a few minutes at most. Conversely, radiosondes have a much higher vertical spatial resolution than most models can use. Modelers generally reduce the vertical resolution of the soundings by averaging over the vertical layers of the model. Recently, Radiometries Corporation (Boulder, Colorado, USA) developed a 12-channel, ground-based microwave radiometer capable of providing continuous, real-time vertical profiles of temperature, water vapor, and limited-resolution cloud liquid water from the surface to 10 km in nearly all weather conditions. The microwave radiometer profiler (MWRP) offers a much finer temporal resolution and reporting interval (about 10 minutes) than the radiosonde but a coarser vertical resolution that may be more appropriate for models. Profiles of temperature, water vapor, and cloud liquid water are obtained at 47 levels: from 0 to 1 km above ground level at 100-m intervals and from 1 to 10 km at 250-m intervals. The profiles are derived from the measured brightness temperatures with neural network retrieval. In Figure 1, profiles of temperature, water vapor, and cloud liquid water for 10 May 2000 are presented as time-height plots. MWRP profiles coincident with the 11:31 UTC (05:31 local) and 23:47 UTC (17:47 local) soundings for 10 May are presented in Figures 2 and 3, respectively. These profiles illustrate typical performance for temperature inversion and lapse conditions

The Effect of Cumulus Cloud Field Anisotropy on Domain-Averaged Solar Fluxes and Atmospheric Heating Rates

Cumulus clouds can become tilted or elongated in the presence of wind shear. Nevertheless, most studies of the interaction of cumulus clouds and radiation have assumed these clouds to be isotropic. This paper describes an investigation of the effect of fair-weather cumulus cloud field anisotropy on domain-averaged solar fluxes and atmospheric heating rate profiles. A stochastic field generation algorithm was used to produce twenty three-dimensional liquid water content fields based on the statistical properties of cloud scenes from a large eddy simulation. Progressively greater degrees of x-z plane tilting and horizontal stretching were imposed on each of these scenes, so that an ensemble of scenes was produced for each level of distortion. The resulting scenes were used as input to a three-dimensional Monte Carlo radiative transfer model. Domain-average transmission, reflection, and absorption of broadband solar radiation were computed for each scene along with the average heating rate profile. Both tilt and horizontal stretching were found to significantly affect calculated fluxes, with the amount and sign of flux differences depending strongly on sun position relative to cloud distortion geometry. The mechanisms by which anisotropy interacts with solar fluxes were investigated by comparisons to independent pixel approximation and tilted independent pixel approximation computations for the same scenes. Cumulus anisotropy was found to most strongly impact solar radiative transfer by changing the effective cloud fraction, i.e., the cloud fraction when the field is projected on a surface perpendicular to the direction of the incident solar beam

Stratus cloud structure from MM-radar transects and satellite images: scaling properties and artifact detection with semi-discrete wavelet analysis

Spatial and/or temporal variabilities of clouds is of paramount importance for at least two in tensely researched sub-problems in global and regional climate modeling: (1) cloud-radiation interaction where correlations can trigger 3D radiative transfer effects; and (2) dynamical cloud modeling where the goal is to realistically reproduce the said correlations. We propose wavelets as a simple yet powerful way of quantifying cloud variability. More precisely, we use 'semi-discrete' wavelet transforms which, at least in the present statistical applications, have advantages over both its continuous and discrete counterparts found in the bulk of the wavelet literature. With the particular choice of normalization we adopt, the scale-dependence of the variance of the wavelet coefficients (i.e,, the wavelet energy spectrum) is always a better discriminator of transition from 'stationary' to 'nonstationary' behavior than conventional methods based on auto-correlation analysis, second-order structure function (a.k.a. the semi-variogram), or Fourier analysis. Indeed, the classic statistics go at best from monotonically scale- or wavenumber-dependent to flat at such a transition; by contrast, the wavelet spectrum changes the sign of its derivative with respect to scale. We apply 1D and 2D semi-discrete wavelet transforms to remote sensing data on cloud structure from two sources: (1) an upward-looking milli-meter cloud radar (MMCR) at DOE's climate observation site in Oklahoma deployed as part of the Atmospheric Radiation Measurement (ARM) Progrm; and (2) DOE's Multispectral Thermal Imager (MTI), a high-resolution space-borne instrument in sunsynchronous orbit that is described in sufficient detail for our present purposes by Weber et al. (1999). For each type of data, we have at least one theoretical prediction - with empirical validation already in existence - for a power-law relation for wavelet statistics with respect to scale. This is what is expected in physical (i.e., finite scaling range) fractal phenomena. In particular, we find long-range correlations in cloud structure coming from the important nonstationary regime. More surprisingly, we also uncover artifacts the data that are traceable either to instrumental noise (in the satellite data) or to smoothing assumptions (in the MMCR data processing). Finally, we discuss the potentially damaging ramifications the smoothing artifact can have on both cloud-radiation and cloud-modeling studies using MMCR data