Manajit Sengupta

Cloud-Resolving Satellite Data Assimilation: Information Content of IR Window Observations and Uncertainties in Estimation

Abstract This study addresses the problem of four-dimensional (4D) estimation of a cloudy atmosphere on cloud-resolving scales using satellite remote sensing measurements. The motivation is to develop a methodology for accurate estimation of cloud properties and the associated atmospheric environment on small spatial scales but over large regions to aid in better understanding of the clouds and their role in the atmospheric system. The problem is initially approached by the study of the assimilation of the Geostationary Operational Environmental Satellite (GOES) imager observations into a cloud-resolving model with explicit bulk cloud microphysical parameterization. A new 4D variational data assimilation (4DVAR) research system with the cloud-resolving capability is applied to a case of a multilayered cloud evolution without convection. In the experiments the information content of the IR window channels is addressed as well as the sensitivity of estimation to lateral boundary condition errors, model firs...

An Evaluation of Five ARW-WRF Microphysics Schemes Using Synthetic GOES Imagery for an Atmospheric River Event Affecting the California Coast

AbstractThe main purpose of the present study is to assess the value of synthetic satellite imagery as a tool for model evaluation performance in addition to more traditional approaches. For this purpose, synthetic GOES-10 imagery at 10.7 μm was produced using output from the Advanced Research Weather Research and Forecasting (ARW-WRF) numerical model. Use of synthetic imagery is a unique method to indirectly evaluate the performance of various microphysical schemes available within the ARW-WRF. In the present study, a simulation of an atmospheric river event that occurred on 30 December 2005 was used. The simulations were performed using the ARW-WRF numerical model with five different microphysical schemes [Lin, WRF single-moment 6 class (WSM6), Thompson, Schultz, and double-moment Morrison]. Synthetic imagery was created and scenes from the simulations were statistically compared with observations from the 10.7-μm band of the GOES-10 imager using a histogram-based technique. The results suggest that syn...

Synthetic satellite imagery for current and future environmental satellites

During the next decade, data from a new generation of US geostationary and polar orbiting satellites will become available. To prepare for these data, representative imagery of these satellites is desirable. Two independent methods have been developed to create imagery from future satellites before they are placed into orbit. One method uses data from current operational and experimental satellites. Data obtained this way are referred to as simulated imagery. Another method generates satellite imagery by using numerical models. Data obtained by this method are referred to as synthetic imagery. Each method has some weaknesses that can be overcome by using both methods together. Synthetic imagery for two future US sensors is introduced in this paper. Emphasis is placed on a severe thunderstorm event.

Assimilating synthetic GOES-R radiances in cloudy conditions using an ensemble-based method

The weather research and forecasting (WRF) model and the maximum likelihood ensemble filter (MLEF) data assimilation approach are used to examine the potential impact of observations from the future Geostationary Operational Environmental Satellite, generation R (GOES-R) on improving our knowledge about clouds. Synthetic radiances are assimilated from the 10.35 μm channel of the GOES-R advanced baseline imager (ABI) employing a ‘non-identical twins’ experimental setup. The experimental results are examined for an extratropical cyclone named Kyrill that produced unusually strong winds, widespread damage and fatalities in Western Europe in January 2007. The data assimilation problem is especially challenging for this case, as there is a large error in the model-simulated radiances resulting from incorrect cloud location. Although this problem is difficult to eliminate, data assimilation results indicate the potential of GOES-R data to significantly reduce these errors.

PV ramping in a distributed generation environment: A study using solar measurements

Variability in Photovoltaic (PV) generation resulting from variability in the solar radiation over the PV arrays is a topic of continuing concern for those involved with integrating renewables onto existing electrical grids. The island of Lanai, Hawaii is an extreme example of the challenges that integrators will face due to the fact that it is a small standalone grid. One way to study this problem is to take high-resolution solar measurements in multiple locations and model simultaneous PV production for various sizes at those locations. The National Renewable Energy Laboratory (NREL) collected high-resolution solar data at four locations on the island where proposed PV plants will be deployed in the near future (Fig. 1). This data set provides unique insight into how the solar radiation may vary between points that are proximal in distance, but diverse in weather, due to the formation of orographic clouds in the center of the island. Using information about each proposed PV plant size, power output was created at high resolution. The team analyzed this output to understand power production ramps at individual locations and the effects of aggregating the production from all four locations. Hawaii is a unique environment, with extremely variable events occurring on a daily basis. This study provided an excellent opportunity for understanding potential worst-case scenarios for PV ramping. This paper provides an introduction to the datasets that NREL collected over a year and a comprehensive analysis of PV variability in a distributed generation scenario.

Estimating Three-Dimensional Cloud Structure via Statistically Blended Satellite Observations

AbstractThe launch of the NASA CloudSat in April 2006 enabled the first satellite-based global observation of vertically resolved cloud information. However, CloudSat’s nonscanning W-band (94 GHz) Cloud Profiling Radar (CPR) provides only a nadir cross section, or “curtain,” of the atmosphere along the satellite ground track, precluding a full three-dimensional (3D) characterization and thus limiting its utility for certain model verification and cloud-process studies. This paper details an algorithm for extending a limited set of vertically resolved cloud observations to form regional 3D cloud structure. Predicated on the assumption that clouds of the same type (e.g., cirrus, cumulus, and stratocumulus) often share geometric and microphysical properties as well, the algorithm identifies cloud-type-dependent correlations and uses them to estimate cloud-base height and liquid/ice water content vertical structure. These estimates, when combined with conventional retrievals of cloud-top height, result in a 3...

Validating an operational physical method to compute surface radiation from geostationary satellites

Models to compute global horizontal irradiance (GHI) and direct normal irradiance (DNI) have been in development over the last three decades. These models can be classified as empirical or physical based on the approach. Empirical models relate ground-based observations with satellite measurements and use these relations to compute surface radiation. Physical models consider the physics behind the radiation received at the satellite and create retrievals to estimate surface radiation. While empirical methods have been traditionally used for computing surface radiation for the solar energy industry, the advent of faster computing has made operational physical models viable. The Global Solar Insolation Project (GSIP) is a physical model that computes DNI and GHI using the visible and infrared channel measurements from a weather satellite. GSIP uses a two-stage scheme that first retrieves cloud properties and uses those properties in a radiative transfer model to calculate GHI and DNI. Developed for polar orbiting satellites, GSIP has been adapted to NOAAs Geostationary Operation Environmental Satellite series and can run operationally at high spatial resolutions. This method holds the possibility of creating high quality datasets of GHI and DNI for use by the solar energy industry. We present an outline of the methodology and results from running the model as well as a validation study using ground-based instruments.

Comparison between observed and synthetic 6.5 and 10.7 μm GOES-12 imagery of thunderstorms that occurred on 8 May 2003

Over the past few years, a numerical system to produce synthetic satellite images has been developed at the Cooperative Institute for Research in the Atmosphere. This is being done to better understand imagery from current and future sensors. This system consists of a mesoscale model and an observational operator. Synthetic imagery of a boundary layer capped stratus cloud and an idealized thunderstorm have been produced by past investigators. In this publication, this system was applied to a thunderstorm event that occurred over the central plains of the USA on 8 May 2003. The main purpose of this study is to extend previous research by comparing observed and synthetic GOES-12 imagery of thunderstorms from an observed event. Synthetic 6.5 and 10.7 μm GOES-12 satellite imagery was produced and compared to actual 6.5 and 10.7 μm GOES-12 imagery from 8 May 2003. Multiple two-way interactive nested grids and two-moment microphysics were employed in this study. Various statistics were used to compare synthetic satellite imagery with observed satellite imagery. Results show that the synthetic imagery was reasonably similar to observed imagery. An approximate 2% cold bias, though, was evident in the synthetic imagery associated with the tops of the simulated thunderstorms. When the calculation of brightness temperatures was done a second time, the number of vertical levels was increased an order of magnitude: the 2% cold bias remained. This led to the conclusion that the bias was related to simulated thunderstorms that were more intense than observed thunderstorms and possibly cooler simulated tropopause temperatures.

Chapter 3 – Physically Based Satellite Methods

Ephemeral clouds and atmospheric aerosols pose the greatest challenges in exploiting sunlight as a viable (both stable and reliable) source of energy. The passage of cloud shadows across a solar array results in significant fluctuations, or ramps, in available energy, while scattering aerosols redistribute direct and diffuse components of solar irradiance in a subtle but pervasive and more sustained way. The timescales of these fluctuations are highly diverse, varying from seconds, in the case of fair-weather cumulus clouds, to hours, in the case of a prefrontal cirrus shield, and to days or more in association with aerosol loading within a synoptic-scale air mass. The spectrum of spatial scale for aerosol and cloud parameters is broad, and monitoring from terrestrially based systems is an inherently ill-posed problem from the standpoints of cost and coverage. Here, satellite-based observations, particularly those from geostationary platforms capable of monitoring the temporal evolution of clouds, provide unique and indispensable capabilities with regard to solar-energy forecasting and resource assessment. In this chapter, we provide a high-level cross-section of environmental satellite observing systems and considerations for their application to quantitative, physically based estimates of solar irradiance at the surface for use in solar forecasting.

Building the Sun4Cast System: Improvements in Solar Power Forecasting

AbstractAs integration of solar power into the national electric grid rapidly increases, it becomes imperative to improve forecasting of this highly variable renewable resource. Thus, a team of researchers from the public, private, and academic sectors partnered to develop and assess a new solar power forecasting system, Sun4Cast. The partnership focused on improving decision-making for utilities and independent system operators, ultimately resulting in improved grid stability and cost savings for consumers. The project followed a value chain approach to determine key research and technology needs to reach desired results.Sun4Cast integrates various forecasting technologies across a spectrum of temporal and spatial scales to predict surface solar irradiance. Anchoring the system is WRF-Solar, a version of the Weather Research and Forecasting (WRF) numerical weather prediction (NWP) model optimized for solar irradiance prediction. Forecasts from multiple NWP models are blended via the Dynamic Integrated Fo...