Cliff Mass University of Washington

Slides:



Advertisements
Similar presentations
Section 2: The Planetary Boundary Layer
Advertisements

Consortium October 4, Hits: Canadians Don’t Give Up.
Intense Spring Sea Breezes Along the New York - New Jersey Coast Stanley David Gedzelman and Kwan-Yin Kong EAS Department and NOAA CREST Center City College.
The Problem of Parameterization in Numerical Models METEO 6030 Xuanli Li University of Utah Department of Meteorology Spring 2005.
Advanced Research WRF High Resolution Simulations of Hurricanes Katrina, Rita and Wilma (2005) Kristen L. Corbosiero, Wei Wang, Yongsheng Chen, Jimy Dudhia.
A Cloud Resolving Model with an Adaptive Vertical Grid Roger Marchand and Thomas Ackerman - University of Washington, Joint Institute for the Study of.
Hongli Jiang 1,3, Michelle Harrold 2,3 and Jamie Wolff 2,3 1: NOAA/ESRL/CIRA, Colorado State University 2: NCAR/Research Applications Laboratory 3: Developmental.
Sensitivity of High-Resolution Simulations of Hurricane Bob (1991) to Planetary Boundary Layer Parameterizations SCOTT A. BRAUN AND WEI-KUO TAO PRESENTATION.
Danielle M. Kozlowski NASA USRP Intern. Background Motivation Forecasting convective weather is a challenge for operational forecasters Current numerical.
Regional Modeling Update March 31, Who is using our model output?
Update on the Northwest Regional Modeling System Cliff Mass, Dave Ovens, Jeff Baars, Mark Albright, Phil Regulski, Dave Carey University of Washington.
Atmospheric Stability. Why is this Important? A stable atmosphere is associated with air pollution, fog, and strong surface temperature inversions in.
Weather Model Background ● The WRF (Weather Research and Forecasting) model had been developed by various research and governmental agencies became the.
IS WRF REALLY IMPROVING? A COMPREHENSIVE VERIFICATION OVER THE PACIFIC NORTHWEST Cliff Mass and David Ovens University of Washington.
Fixing WRF’s High Speed Wind Bias: A New Subgrid Scale Drag Parameterization and the Role of Detailed Verification Cliff Mass and Dave Ovens University.
Boundary Layer Parameterization Issues in the MM5 and Other Models. Cliff Mass, Eric Grimit, Jeff Baars, David Ovens, University of Washington.
Northwest AIRQUEST 12/4/06 Cliff Mass University of Washington.
Consortium Meeting June 3, Thanks Mike! Hit Rates.
Cold Air Damming. Cold Air Damming What is Cold Air Damming?
The National Environmental Agency of Georgia L. Megrelidze, N. Kutaladze, Kh. Kokosadze NWP Local Area Models’ Failure in Simulation of Eastern Invasion.
Development of WRF-CMAQ Interface Processor (WCIP)
19 December ConclusionsResultsMethodologyBackground Chip HelmsSensitivity of CM1 to Initial θ' Magnitude and Radius Examining the Sensitivity of.
28 Jan 1815 UTC2135 UTC Clear patches due to canyon drainage/ Exchange from Utah Valley? PCAPS IOP January 2011.
Prediction of Atlantic Tropical Cyclones with the Advanced Hurricane WRF (AHW) Model Jimy Dudhia Wei Wang James Done Chris Davis MMM Division, NCAR Jimy.
Consortium Meeting Feb 2, Hit Rates Big Snowstorm Hi: 3-4x normal demand. Slowed down web server-new fixes for this.
Richard Rotunno NCAR *Based on:
Dispersion conditions in complex terrain - a case study of the January 2010 air pollution episode in Norway Viel Ødegaard Norwegian Meteorological.
WRF Four-Dimensional Data Assimilation (FDDA) Jimy Dudhia.
WRF Problems: Some Solutions, Some Mysteries Cliff Mass and David Ovens University of Washington.
Low stratus (and fog) forecast for Central Europe introducing an empirical enhancement scheme for sub-inversion cloudiness Harald Seidl and Alexander Kann.
Bogdan Rosa 1, Marcin Kurowski 1 and Michał Ziemiański 1 1. Institute of Meteorology and Water Management (IMGW), Warsaw Podleśna, 61
Update on the Northwest Regional Modeling System 2015 Cliff Mass and David Ovens University of Washington.
Modeling and Evaluation of Antarctic Boundary Layer
Numerical Investigation of Air- Sea Interactions During Winter Extratropical Storms Presented by Jill Nelson M.S. Marine Science Candidate Graduate Research.
A Thermal Plume Model for the Boundary Layer Convection: Representation of Cumulus Clouds C. RIO, F. HOURDIN Laboratoire de Météorologie Dynamique, CNRS,
Consortium Meeting 6/4/2015. Usage is Up Some Additions BUFKIT files available for consortium users New ventilation graphics (deep stable layer graphics)
Mattias Mohr, Johan Arnqvist, Hans Bergström Uppsala University (Sweden) Simulating wind and turbulence profiles in and above a forest canopy using the.
Consortium Meeting Feb 07, Our Audience, Hits: January 2013.
A Case Study of Decoupling in Stratocumulus Xue Zheng MPO, RSMAS 03/26/2008.
Implementation of a boundary layer heat flux parameterization into the Regional Atmospheric Modeling System Erica McGrath-Spangler Dept. of Atmospheric.
The effect of tides on the hydrophysical fields in the NEMO-shelf Arctic Ocean model. Maria Luneva National Oceanography Centre, Liverpool 2011 AOMIP meeting.
Matt Vaughan Class Project ATM 621
Northwest Modeling Consortium
WRF Four-Dimensional Data Assimilation (FDDA)
Grid Point Models Surface Data.
Update on the Northwest Regional Modeling System 2013
University of Washington Ensemble Systems for Probabilistic Analysis and Forecasting Cliff Mass, Atmospheric Sciences University of Washington.
Ben Green Group meeting, 9/13/2013
The ability for the ocean to absorb and store energy from the sun is due to… The transparency of the water that allows the sun’s ray to penetrate deep.
Coupled atmosphere-ocean simulation on hurricane forecast
Atmospheric Stability
Mesoscale “Surprises” in Complex Terrain Revealed by Regional Climate Simulations Cliff Mass, Atmospheric Sciences University of Washington.
Mark A. Bourassa and Qi Shi
Air Pollution and Control (Elective- I)
Northwest Modeling Consortium December 3, 2013
Update on the Northwest Regional Modeling System 2017
Overall Statistics RMSE WRF-UA: 159 W m-2 WRF-UCSD: 171 W m-2 STDERR
The West Coast Thermal Trough
Models of atmospheric chemistry
INFLUX: Comparisons of modeled and observed surface energy dynamics over varying urban landscapes in Indianapolis, IN Daniel P. Sarmiento, Kenneth Davis,
Cliff Mass and David Ovens
Cliff Mass and David Ovens
Short Term forecasts along the GCSS Pacific Cross-section: Evaluating new Parameterizations in the Community Atmospheric Model Cécile Hannay, Dave Williamson,
Consortium Meeting June 14th 2012
Cliff Mass and David Ovens University of Washington
Conrick, R., C. F. Mass, and Q. Zhong, 2018
The Meteorology Leading up to and on Measurement Day
Kurowski, M .J., K. Suselj, W. W. Grabowski, and J. Teixeira, 2018
Xu, H., and X. Li, 2017 J. Geophys. Res. Atmos., 122, 6004–6024
Consortium Meeting June 6, 2019
Presentation transcript:

Cliff Mass University of Washington Improved Subgrid Drag or Hyper PBL/Horizontal Resolution: What Improves the PBL in WRF? Cliff Mass University of Washington

The Problem Many of us have found several related problems with WRF during stable conditions: Inability to maintain shallow cold air layers near the surface Overmixing during stable conditions. Winds too strong and geostrophic at the surface Over the Northwest we have tested the available PBL schemes in WRF and none solved this problem.

The Problem is Obvious in Wind Histograms

A new drag surface drag parameterization Last meeting I reported on a promising approach---increasing the surface roughness dependent on the variability of the subgrid scale terrain. Plausible that current PBL scheme are missing the drag of features that are not resolved. In our testing this summer we found that although it worked during the winter, we lost runs during the summer. Switched to the closely related u*, but still had problems. By changing, parameters in surface layer, messing up surface fluxes under less stable conditions.

A Partial Fix: A New Low Level Drag Parameterization Consulting with Jimy Dudhia of NCAR came up with an approach—enhancing u* in the YSU boundary layer scheme, with the enhancement proportional to the subgrid terrain variance. The idea was that the model was missing the drag from subgrid terrain elements. No changes over water.

k

The Initial Results Looked Quite Favorable Good enough that we went operational with it in late 2010 for the UW WRF 36, 12, and 4 km domains. Not in the UW 1.3 km domain.

Jan1-Feb8 Wind Speed Bias (00 UTC)

But there were issues… Although overall the impact was highly positive, there we were hurting the results in some situations Our WRF runs were underplaying high wind situations. The new drag made it worse. There seemed to be too much drag added during the summer days. This all made some sense since if the atmosphere has a lot of mixing, surface drag elements will be less important.

10 m wind bias (>=20 kt) Winter: 00 UTC

With parameterization (>=20 kt)

Summer: All Winds

With parameterization worse inland where lots of heating and mixing Summer 00TC

But during morning (12 UTC) we were helping

So why not make an alteration to the parameterization So why not make an alteration to the parameterization? Have it back off when the winds are strong or the vertical sounding indicates things are well mixed? Tried two approaches: pull back with strong wind or pull back with either strong wind or well-mixed sounding

Results We help the high wind situations, but hurt with the lower wind speeds.

Dealing with the stable PBL problem Would hyper-resolution help? Version 3.3 of WRF and later allows adding more levels in PBL without it going unstable Inspired by overmixing last December and January. Tried an extra 10 levels below 200 m and 1000m. Tried a few December 2011 dates and several in January 2012.

Adding ten more levels below 200 meters (12 UTC 20 January

Observed

Conclusions Sub-grid scale drag parameterization is highly beneficial, but does hurt when atmosphere is well mixed. Can shut it down when mixing is strong, with modest benefits, mainly at high wind speeds. Hyperresolution in PBL helps model surface based inversions and fog, but otherwise little real impact.