2.4      The MAD-WRF solar irradiance nowcasting model: model overview and evaluation of the cloud initialization system.

Jimenez, P.A , G. Thompson, J. Dudhia and J.A. Lee, National Center for Atmospheric Research

Short term predictions of up to a few hours (i.e. nowcasting) of solar irradiance require accurate cloud initialization and representation of cloud evolution. Standard nowcasting methodologies include the use of retrievals from earth observing satellites to detect and advance the clouds. This methodology faces limitations when microphysical processes control the cloud evolution or in cases wherein clouds are anchored to terrain features. These processes are better represented in numerical weather prediction (NWP) models. However, nowcasting systems based on NWP models do not always include accurate satellite-based cloud initialization. In addition, the clouds may dissipate if the initial conditions are not favorable to sustain the clouds. To overcome these limitations, we are integrating the Multi-Advection and Diffusion Nowcast (MADCast) methodology of using a satellite-based initialization and WRF to advect and diffuse microphysics quantities, with the physics of the WRF-Solar NWP model to create an improved end-to-end solar irradiance forecast system, called MAD-WRF.

This presentation will provide an overview of the MAD-WRF model and an evaluation of the cloud initialization system. The MAD-WRF cloud initialization is based on observations of the cloud base / top height combined with a cloud initialization parameterization. The parameterization relies on relative humidity to identify the location of clouds and estimate the liquid water and ice content. Alternatively, the parameterization can be used on top of hydrometeors, if available, to enhance the initial cloud field. The performance of the initialization is enhanced by assisting the cloud identification scheme with cloud base / top height observations. The benefits of this cloud initialization system are herein quantified with hourly WRF-Solar simulations spanning the month of April 2018.  The benefits of using cloud top height retrievals from GOES16 and cloud base height observations from METAR stations are also investigated. Results are compared against global horizontal irradiance observations from the SURFRAD network and illustrate the added value of the MAD-WRF cloud initialization system.