VARANAL
Constrained Variational Analysis
Baseline VAP, Evaluation VAP, External VAP, Guest
The large-scale forcing data is derived based on the constrained variational analysis approach (Zhang and Lin, 1997; Zhang et al. 2001), which calculates the large-scale vertical velocity and advective tendencies from sounding measurements of winds, temperature, and water vapor mixing ratio over a network of a small number of stations.
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Major updates in the VARANAL v2 include: 1) the method used to develop multi-year continuous forcing data, 2) the incorporation of eddy correlation flux (ECOR) measurement system turbulent fluxes into the analysis, and 3) improvements to the workflow to increase efficiency.
Currently, two major VARANAL data sets are archived by ARM: 1) Radiosonde- or numerical weather prediction (NWP)-based forcing data for short-term Intensive Operational Periods (IOPs) at different ARM fixed or mobile sites. Most of the IOP forcing is at 3-hr and 25-mb resolutions. 2) Multi-year continuous forcing data at the ARM observatories. The continuous forcing is at 1-hr and 25-mb resolutions.
This VAP has been used to drive single-column models (SCMs), cloud-resolving models (CRMs), and large-eddy simulation (LES) models for different cloud and convective systems. Results from these model simulations are then used to improve cloud parameterizations in earth system models. It can also be applied to evaluate model results, as it includes diagnostic fields such as diabatic heating profiles, cloud fields, surface measurements, and large-scale conditions. In addition, VARANAL is one of the critical data sets required for the ongoing routine LES ARM Symbiotic Simulation and Observation (LASSO) activity.
The derivations of the VARANAL from field measurements are subject to uncertainties that can directly impact the simulated cloud and radiation fields by SCM/CRM/LES. These uncertainties originate from two sources: 1) the instrument and measurement errors, and 2) the scale aliasing or sampling biases errors. Both error types depend on scales because horizontal derivatives are involved in the calculation of the horizontal fluxes. Ensemble forcing data by perturbing potential uncertainties in the constraints can help address this type of uncertainty in the forcing data. Please refer to the technical report for more information.
Primary Derived Measurements
- Advective tendency
- Atmospheric moisture
- Atmospheric pressure
- Atmospheric temperature
- Cloud fraction
- Cloud size
- Cloud top height
- Horizontal wind
- Longwave broadband downwelling irradiance
- Longwave broadband net irradiance
- Longwave broadband upwelling irradiance
- Shortwave broadband total downwelling irradiance
- Shortwave broadband total net irradiance
- Shortwave broadband total upwelling irradiance
- Net broadband total irradiance
- Latent heat flux
- Liquid water path
- Precipitation
- Precipitable water
- Sensible heat flux
- Surface skin temperature
- Soil temperature
- Vertical velocity
Contact
View all contacts-
Shaocheng XieTranslator Lawrence Livermore National Laboratory
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Minghua ZhangScience Sponsor PI Stony Brook University
Related Data Announcements
References
View all references- Xie et al. "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". 2004. 10.1029/2003jd004045.
- Zhang et al. "Objective Analysis of ARM IOP Data: Method and Sensitivity". 2001. 10.1175/1520-0493(2001)129<0295:oaoaid>2.0.co;2.
- Zhang et al. "Constrained Variational Analysis of Sounding Data Based on Column-Integrated Budgets of Mass, Heat, Moisture, and Momentum: Approach and Application to ARM Measurements". 1997. 10.1175/1520-0469(1997)054<1503:cvaosd>2.0.co;2.
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