Developing long‐term single‐column model/cloud system–resolving model forcing data using numerical weather prediction products constrained by surface and top of the atmosphere observations

Abstract

[1] This study proposes another approach to develop multiyear single-column model (SCM) and cloud system-resolving model (CSRM) forcing data from numerical weather prediction (NWP) model analyses constrained with the observed surface and top of the atmosphere measurements by using a variational analysis approach. In the approach the atmospheric state variables from NWP analyses are adjusted to balance the observed column budgets of mass, heat, moisture, and momentum rather than the NWP modelproduced budgets. The derived constrained NWP forcing data from the National Oceanic and Atmospheric Administration rapid update cycle (RUC) analyses are evaluated by the ''observed'' forcing data derived from radiosonde and wind profiler data collected at the Atmospheric Radiation Measurement (ARM) Program Southern Great Plains site under three selected cases: A strong convective case in the ARM summer 1997 intensive operational period (IOP), a moderate synoptic-scale process-dominated precipitation period in the spring 2000 IOP, and a nonprecipitation period in the late fall 2000 IOP. We show that the forcing data derived from the RUC analyses using ARM column constraints agree with the observed forcing reasonably well. The largest improvements are seen during precipitation periods since precipitation is a strong constraint used in the proposed approach. During the nonprecipitation period the improvements are moderate because the constraints are weak in the absence of precipitation. The constrained NWP forcing and the observed forcing, however, show better agreement during the moderate precipitation period and the nonprecipitation period than during the strong convective period. In SCM tests we show that most model errors revealed by the SCM driven by the observed forcing can be seen in the SCM driven by the constrained NWP forcing. These results suggest the feasibility of using the derived constrained NWP forcing data from RUC analyses for statistical studies of SCM/CSRM results over long time periods.

Large-scale vertical velocity, diabatic heating and drying profiles associated with seasonal and diurnal variations of convective systems observed in the GoAmazon2014/5 experiment

“…The upper-level fields from ECMWF analysis data are adjusted to conserve the vertical integration of mass, moisture and dry static energy through a constrained variational analysis technique described in Zhang and Lin (1997) and Zhang et al (2001). As indicated in Xie et al (2004), the use of the surface and TOA observations as constraints improves the quality of the large-scale vertical velocity and budgets in operational analysis data and makes the data suitable for budget analysis and cloud modeling studies. An important byproduct of this study is the derived large-scale forcing data (ARM Climate Research Facility, 2001) supporting modeling studies, which are available to the community at the Atmospheric Radiation Measurement (ARM) program archive (http://iop.archive.arm.gov/ arm-iop/0eval-data/xie/scm-forcing/iop_at_mao/).…”

Three‐dimensional constrained variational analysis: Approach and application to analysis of atmospheric diabatic heating and derivative fields during an ARM SGP intensive observational period

“…The Rapid Update Cycle (RUC) [Benjamin et al, 2004] is a 3D-VAR operational regional analysis from NCEP. It is also used in 1DCVA to produce continuous forcing data [Xie et al, 2004]. The analysis used in this paper is RUC2 (implemented from year 1998 to 2002) with 40 km horizontal resolution, 40 vertical levels, and 1 h assimilation frequency.…”

Piecewise log-normal approximation of size distributions for aerosol modelling

“…Results for temperature and humidity were additionally nudged towards analyzed temperature and humidity profiles. The analysis of meteorological results is based on numerical weather prediction products with additional constraints from surface and top-of-the-atmosphere measurements (Xie et al, 2004).…”