Gael Descombes

Improved modeling of cloudy‐sky actinic flux using satellite cloud retrievals

Clouds play a critical role in modulating tropospheric radiation and thus photochemistry. We develop a methodology for calculating the vertical distribution of tropospheric ultraviolet (300–420 nm) actinic fluxes using satellite cloud retrievals and a radiative transfer model. We demonstrate that our approach can accurately reproduce airborne-measured actinic fluxes from the 2013 Studies of Emissions and Atmospheric Composition, Clouds and Climate Coupling by Regional Surveys (SEAC4RS) campaign as a case study. The actinic flux is reduced below optically moderate-thick clouds inversely with cloud optical depth, and can be enhanced by a factor 2 above clouds. Inside clouds, the actinic flux can be enhanced by 2–3 times in the upper part of clouds or reduced by 90% in the lower parts of clouds. Our study suggests that the use of satellite-derived actinic fluxes as input to chemistry-transport models can improve the accuracy of photochemistry calculations.

Multi-sensor Advection Diffusion nowCast (MADCast) for cloud analysis and short-term prediction

A new approach designed for the analysis and short-term forecasting of clouds, called Multi-sensor Advection-Diffusion nowCast (MADCast), has been implemented within the Weather Research and Forecasting (WRF) model and data assimilation platforms. In this approach, profiles of cloud fractions are retrieved from multiple infrared sensors using the Multivariate Minimum Residual (MMR) scheme. These profiles are then projected to the grid of the numerical weather prediction model, which is used to dynamically transport and diffuse the clouds in three dimensions. NCAR/MMM MADCast system Technical note 2/21 LIST OF CONTENTS 1. INTRODUCTION ..............................................................................................................................4 2. METHODOLOGY ....................................................................................................... 4 2.1 Cloud fraction retrieval ................................................................................................... 5 2.2 3-D Multi sensor approach.............................................................................................. 6 2.3 Forecast and rapid update cycling................................................................................... 7 3. IMPLEMENTATION ............................................................................................... 9 3.1 Processing of all-sky radiances from multiple sensors ................................................... 9 3.2 Modification of the Community Radiative Transfer Model (CRTM) ............................ 9 3.3 Cloud Analysis .............................................................................................................. 10 3.4 WRF forecast ................................................................................................................ 14 4. EXPERIMENT SET-UP......................................................................................... 15 4.1 Software Installation ..................................................................................................... 15 4.2 Input .............................................................................................................................. 15 4.3 Output............................................................................................................................ 15 4.4 Namelist ........................................................................................................................ 16 5. VERIFICATION..................................................................................................... 18 5.1 Cloud fraction................................................................................................................ 19 5.2 Solar irradiance ............................................................................................................. 20 6. CONCLUSIONS..................................................................................................... 20 NCAR/MMM MADCast system Technical Note 3/21 LIST OF FIGURES Figure 1, Flowchart of the MADCast system, which is composed of three main steps: first, it retrieves vertical profile of cloud fraction by pixel from various InfraRed (IR) sensors, then it combines them to perform a single gridded analysis of cloud fraction, and finally a forecast is launched and cycling is allowed. .......................... 5 Figure 2. (a) Size of the interpolation radius as a function of scan angle for IASI. (b) Example of IASI fields of view on the edge of the swath. The 4x4 pixel matrix explains the shape of the curve in (a)......................................................................... 6 Figure 3. (a) Example of raw cloud fraction for IASI. (b) Example of IASI cloud fraction after interpolation. ........................................................................................ 7 Figure 4, Forecast of vertically integrated cloud fraction (in %) from the WRF model at a) t+0h, b) t+1h, c) t+3h and d) t+6h. ........................................................................ 8 Figure 5. Flowchart of the cloud fraction retrieval process, which is performed in an iterative way over the pixels of all the available sensors. First, the MMR scheme computes observed, modelled and overcast radiance by pixel of a sensor. Then, from a first guess defined in cloud fraction, it minimizes a cost function defined in terms of radiance to ultimately interpolate the analysis onto the horizontal grid, thus filing out the gap between pixels. ............................................................................ 11 Figure 6. Flowchart of MADCast post-processing system. The 3-D cloud fraction verification is performed by matching a cloud mask determined from GOES imager satellite data with a cloud mask of WRF determined from the model output. Comparisons of irradiance are done at the location of the SURFRAD/ISIS ground station network......................................................................................................... 18 Figure 7. (a) Example of scores calculated from the regridded observed and modelled cloud masks over a single 6-hour forecast. Maps of contingency table for (b) t+0h, and (c) t+6h. ............................................................................................................. 19 Figure 8. Time series of 10-min averaged Global Horizontal Irradiance (GHI) at Penn State University SURFRAD station (UTC time) for observations (black), model analysis (blue), the 2-h forecast (green), and 5-h forecast (orange). The red line shows the clear sky model simulation. .................................................................... 20