Climate Forcing Measurements for Research and Validation

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The Urban Atmosphere Research Program Focus
To take our forecasts and analyses to where
people live and work. Issues -- For
forecasting Dispersion Floods Fronts
Heat waves Cold spells Icing Fires Air
Quality For data acquisition and
analysis Climate Ecosystem loading Data
requirements are different for the various
issues. A central requirement is for an improved
capability to predict the surface boundary layer
accurately, and for data to support it.
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• Forecasting extreme weather the probability
  of experiencing hazardous heat waves, extreme
  cold spells, icing and frontal passage (a linkage
  with the USWRP).
• For flooding taking the spatial variability
  of precipitation into proper account.
• For air quality the probability of exceeding
  potentially dangerous levels, e.g. of ozone and
  particulate matter (c.f. EPA/NOAA air quality
  forecasting).
• For dispersion the probability that people
  will be harmed by releases directly into the
  local atmosphere.
• For urban fires local wind behavior, with an
  emphasis on gustiness (and precipitation, of
  course)
• We need to learn how to forecast, using local
  data as guidance. We need to be providing
  information that can be used. We need to avoid
  raising false alarms.
• A sobering realization more consideration of
  the surface is required than for weather
  forecasting, and there are relatively few field
  studies.

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At the Multi-Agency level -- the evolving Urban
Test Bed Research Program (building upon the OFCM
Challenges in Urban Meteorology conference,
September 2004.) The scientists already talk
the agencies are starting DHS DOE DTRA
Army EPA DOI Main areas receiving attention
Washington, DC New York (Las
Vegas?) Oklahoma City Salt Lake
City Principal groups involved National
Laboratories (ANL, PNL, LLBL, LLNL, BNL, ORNL,
Sandia) NOAA (NWS, NOS, ARL, ESRL NSSL) EPA
(NERL) Academic linkages So far, through
individual university contacts, the AMS and the
International Association for Urban Climate
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Street canyons present great difficulty. What
should we predict -- C or P(C gt
C0)? Skimming flow is driven by the
meteorology aloft. The in-canopy environment is
controlled by the configuration of streets and
buildings, traffic patterns, etc.
Washington, DC, and New York illustrate two
extremes

• Washington has broad streets and low buildings.
• New York has deep and sometimes narrow street
  canyons.

Physical models and computational fluid dynamics
models are being used, in parallel.
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http//dcnet.atdd.noaa.gov/
A vision for measurements --
The proposed UrbaNet configuration
Tier 1 -- Surface towers 1a Conventional
meteorological systems 1b Enhanced systems 1c
Specialized systems Tier 2 Remote sensing
(RADAR, etc.)
Rooftop Towers (1c)
U
RADAR LIDAR (2)
Avg. bldg height
T
T
T
T Surface Towers (1a, 1b)
UrbaNet is DCNet in other areas (Tier 1c).
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Some challenges for NOAA 1. Accept the
dominant mission given to us to provide
forecasts to protect life, property and the
environment, related to the atmosphere and the
oceans. This includes forecasts of
dispersion. 2. Take forecasts to where the
predictions affect people, where they live and
work. Go probabilistic? 3. Emphasize areas
where people and the environment are at risk
cities, industrial areas, ports, entertainment
areas, etc. Hard reality check 1
Meteorological models are constructed from the
understanding of processes, each of which is
represented as an average behavior. If the
models are essentially built from understanding
of averages, they should not be expected to apply
except on the average. We are no longer
interested solely in the average. We need to
address specific instances. Hence, the
requirement for more data is extreme.
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Hard reality check 2 Meteorological models
are becoming increasingly refined. However in
daytime the convective process in the boundary
layer is largely stochastic and hence
deterministic models should not be expected to
reproduce behaviors on the scale of convective
updrafts, except on the average. Hence,
conventional mesoscale models should not agree
well with observations taken over scales that are
less than several kilometers, unless these
observations are ensemble-averaged. Hard
reality check 3 In theory and in practice,
micrometeorological descriptions of the surface
apply above about ten times the roughness length
(several m) above the zero plane ( 80 of the
average structure height). Typically RADAR
cannot look low enough, and towers are not high
enough. One Modelers response Model output
often agree well with observations. This is
because models have been calibrated to the
observations. However, Large ensemble effort
underway to quantify uncertainties from the
schochastic and other poorly represented
processes.
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NOAA Modeling Applications

• Provide representation of the skimming flow
• Incorporate urban scale parameterizations in
  mesoscale models
• Focused evaluation of NWS non-hydrostatic model
  forecasts (4 km) with urban datasets
• Provide high resolution meteorological
  uncertainties using ensemble techniques
• Explore mesoscale data assimilation techniques
  with urban datasets

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The NMM-WRF Modeling System

• Regional-Scale, Moving Multiple Nest,
  Atmospheric Modeling System.
• Non-Hydrostatic system of equations formulated on
  a rotated latitude-longitude, Arakawa E-grid and
  a vertical, pressure hybrid (sigma_p-P)
  coordinate.
• Designed for 1-10 km horizontal resolution scales
• Runs operationally at NCEP for
• Daily High Resolution Window cycles
• Special Fire Weather nests
• Homeland Security Nests
• 2005 inauguration, 2006 Torino Olympics

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The NMM-WRF Modeling System
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Metro-Watch(Gopalakrishnam)
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Salient Features Telescopic E-Grid

• Large Scale portion of the flow may be easily
  separated from the small scale structure which
  may be advantageous for Hurricane analysis.
• However, as pointed out by Zhang et al.(1986
  MWR), for the sake of smooth solutions across the
  interfaces it may be necessary to sacrifice mass
  and energy conservation across the interface in
  this approach. Nevertheless, for short-term
  numerical forecasts in which the use of
  appropriate model physics and the patterns to be
  forecast may be important than exact mass and
  energy conservation, as long as the mass (or
  energy) discrepancy at the interface is small.

Courtesy of Gopalakrishnam, EMC
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Real-Time Mesoscale Analysis(Geoff DiMego, EMC)

• The RTMA is a fast-track, proof-of-concept effort
  intended to
• leverage and enhance existing analysis
  capabilities in order to generate experimental
  CONUS-scale hourly NDFD-matching analyses
• OPERATIONAL at NCEP and on AWIPS with OB7 by end
  of FY2006
• establish a real-time process that delivers a
  sub-set of fields to allow preliminary
  comparisons to NDFD forecast grids
• Hourly temp, wind, moisture plus precip sky on
  5 km NDFD grid
• also provide estimates of analysis uncertainty
• establish benchmark for future AOR efforts
• build constituency for subsequent AOR development
  activities

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RTMA Procedure

• Temperature dew point at 2 m wind at 10 m
• RUC forecast/analysis (13 km) is downscaled by
  FSL to 5 km NDFD grid
• Downscaled RUC used as first-guess in NCEPs
  2DVar analysis of ALL surface observations
• Estimate of analysis error/uncertainty
• Precipitation NCEP Stage II analysis
• Sky cover NESDIS GOES sounder effective cloud
  amount

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RTMA Logistics

• Hourly within 30 minutes
• 5 km NDFD grid in GRIB2
• Operational at NCEP Q3 FY2006
• Distribution of analyses and estimate of analysis
  error/uncertainty via AWIPS SBN as part of OB7
  upgrade end of FY2006
• Archived at NCDC

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Real Time Mesoscale Analysis
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Summary

• Urban mesonets can be useful now for
• Development and evaluation of urban
  parameterizations in mesoscale models
• Assimilation into real-time mesoscale analyses
  and into high resolution nested modeling
• Quantify uncertainties from ensemble predictions
  systems (eg JEFS, SREF, other HR ensemble)

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BACKUPS
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Hard reality check 4 People live within the
surface canopy of trees and buildings, where
the mesoscale models will not apply except on the
average, and where local data are highly
influenced by the local distribution of roughness
elements and/or obstacles. We need to learn
how to forecast for the canopy boundary layer,
below the height at which the mesoscale models
apply. Hard reality check 5 There are
plenty of data available, from instruments
erected within the canopy by transportation
departments, environmental agencies, private
companies, for example. These are usually
situated within the canopy layer. Their data are
not likely to be of use in the context of
conventional mesoscale models, but it is not
these conventional models that are appropriate
for the mission at hand. Hence, we must learn
how to extract relevant data from the large
number of observations now available.
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Hard reality check 6 Date from within the
area of interest are not sufficient, by
themselves. There needs to be supporting
information from the surrounding area, mostly
upwind. Hard reality check 7 We must
re-think the issue of data acceptability.
Synopticians emphasize representativeness, so
that their models and the data have a chance of
converging. They insist (correctly) on data well
above the canopy, and not influenced by local
obstacles. The present application emphasizes
the need for data indicative of conditions within
the canopy. We need data that are accurate
representations of conditions where the
instrumentation is situated. We need to think
in terms of micrometeorological data quality
control, not data quality assurance as designed
for weather forecasting purposes. Q1 Is the
instrumentation reporting what it
experiences? Q2 Is the data stream useful?
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• Rethink tower data acceptability
• There are two main questions. Are the data
  correct, and are they useful? Correctness is
  essentially a Point of Origin question.
• Conduct Point of Origin data assurance,
• -- Perform scheduled calibrations.
• -- Search for data drop-outs.
• -- Inspect the continuous data record and search
  for periods of constant output (indicative of
  sensor failure and/or intermittency)
• -- Report at every reporting interval the
  statistics generated from the raw data
• Averages and standard deviations
• Proportion of time with unchanging output from
  sensor

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• Data Usefulness is a Point of Application
  question.
• 2. Conduct Point of Application data utility
  tests,
• -- Apply such data quality criteria as may be
  relevant for the specific
• application.
• Scrupulously avoid rejecting data because they
  fail to meet the requirements of a different
  application.
• -- Perform tests as needed to demonstrate
  forecasting improvements if the data are used.
• -- Interface with the data network authorities to
  optimize the data collection/transfer/utilization
  processes.
• -- Subject unwelcome critics to such torture as
  might be intellectually satisfying.

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Some relevant heresies Instead of nudging
forecasts with a few observations, why not rely
on nowcasts and adjust these according to
predicted changes? I.e. Start with the best
depiction of current reality, and construct
expectations using the current situation as the
foundation. The vision synopticians will
forecast skimming flow, local experts will
refine forecasts to fit local situations. Rememb
er Meteorology is still somewhat an art. Like
all artists, meteorologists tend to fall in love
with their models.