![](media/image1.emf){width="2.925in"
height="2.627083333333333in"}EXPLICIT INITIALIZATION OF CLOUDS AND
PRECIPITATION

IN MESOSCALE FORECAST MODELS

Brent L. Shaw\*, John A. McGinley, and Paul Schultz

*NOAA Research-Forecast Systems Laboratory*

*Boulder, Colorado*

*\*\[In collaboration with the Cooperative Institute for Research in the
Atmosphere (CIRA),*

*Colorado State University, Fort Collins, Colorado\]*

**1. INTRODUCTION**

In recent years, operational numerical weather prediction (NWP) models
have become more sophisticated in their treatment of microphysical
processes as the cost of high-performance computing has decreased.
Microphysical schemes and computer parameterizations that were once only
used in research are being adopted by operational agencies in the hope
that explicit forecasts of clouds and precipitation will improve. While
this has been the case for the \"day 2\" forecast and beyond, the short
range (0-12 h) period remains the bane of numerical model accuracy due
to the infamous \"spin-up\" problem, despite various attempts to
mitigate this problem using dynamic initialization or more sophisticated
data assimilation systems. Now that fast computing systems allow us to
run mesoscale models and have the output available within 1 or 3 h after
the initialization time, the importance of a useful and accurate
short-range explicit forecast of clouds and precipitation has been
heightened, particularly if this portion of the explicit numerical
forecast is to be used operationally. To address this problem, a new
version of the NOAA Forecast Systems Laboratory\'s (FSL) Local Analysis
and Prediction System (LAPS, Albers et al. 1996) is being used to
diabatically initialize mesoscale NWP models with all microphysical
species present in the initial condition and in dynamic balance with the
mass and momentum fields.

A real-time forecast system based on the new diabatic initialization has
been running routinely at FSL since the fall of 2000, with output
provided on the Internet (http://laps.fsl.noaa.gov) as well as to the
collocated National Weather Service (NWS) Weather Forecast Office (WFO)
in Boulder for operational evaluation. It is also made available on
FSL\'s Advanced Weather Information Processing System (AWIPS)
workstation for use in daily weather briefings.

This paper provides a brief description of the analysis procedure and
some preliminary quantitative verification results compared to other
initialization methods. A qualitative assessment of operational utility
based on feedback from the Boulder WFO is discussed in Shaw et al.
(2001).

**2. THE LAPS ANALYSIS**

LAPS was initially developed over a decade ago and has undergone
continuous upgrades and improvements throughout this time period. It was
designed to provide a means of combining all available meteorological
data sources into a single, coherent three-dimensional depiction of the
atmosphere at high spatial and temporal resolution for use by
operational forecasters. The operational nature of LAPS dictated the
necessity for flexible and reliable data ingest, quality control, and
computational efficiency. LAPS has demonstrated capability in all of
these areas and is now used by a variety of government and private
agencies, both inside and outside of the United States, including every
NWS WFO as part of AWIPS. Within the WFO, LAPS is able to capitalize on
AWIPS\' ability to ingest local data sources (e.g., mesonets, etc.) that
are not routinely available for the regional and global-scale models run
at the national centers.

Within FSL, LAPS is run routinely on a domain covering approximately 1.3
million square kilometers (Figure 1) using 125 by 105 horizontal grid
points with a 10 km grid spacing. This LAPS domain contains 21 pressure
levels with a 50 mb vertical spacing ranging from 1100 mb through 100
mb. On this domain, hourly analyses of the atmospheric state variables,
clouds, precipitation, and a variety of surface variables (snow cover,
accumulated precipitation, etc.) are produced using the following
sources of data: GOES imager and sounder radiances, WSR-88D reflectivity
and VAD winds, NOAA wind profilers, aircraft observations (ACARS and
PIREPs), and surface observations (including various mesonets and
locally acquired data). For use as a first guess, this domain typically
relies on the operational national-scale grids from the National Centers
for Environmental Prediction (NCEP), typically the RUC-2 or Eta model.
Additionally, when configured to initialize a local mesoscale model,
LAPS can use that model\'s output as a first guess so that a full data
assimilation cycle can be established.

Although LAPS has been used for some time to initialize mesoscale NWP
models, it has been used to initialize only the state variables for a
model \"cold start\" or within a pre-forecast period during which the
model is run using analysis \"nudging\" toward the LAPS-analyzed state
variables.

Cram et al. (1995) tested the use of LAPS to initialize clouds for
mesoscale NWP forecasts with limited success. However, the lack of a
dynamic balance between the initial cloud and momentum fields prevented
the clouds from being completely sustained during the early hours of the
forecast.

Two recent improvements to the LAPS analysis system address the
initialization of clouds and precipitation. First, an improved cloud
analysis scheme (Schultz and Albers 2001) provides a three-dimensional
depiction of the water content in all phases (cloud liquid, rain, ice,
snow, and graupel) based on the radar, satellite, and conventional
temperature and moisture analyses. In addition, vertical motion profiles
consistent with the cloud type and depth are derived during this
process.

Second, a dynamic balance package (McGinley and Smart 2001) uses the
analyses of clouds (and their vertical motions) in conjunction with the
initial analyses of the state variables to produce a final analysis
suitable for initializing the forecast model. This balance package uses
a three-dimensional variational (3DVAR) approach to ensure that the
fields of mass and horizontal divergence are consistent with the
cloud-derived vertical motions. The cost function used in this approach
includes terms to ensure mass continuity as well as a minimization of
the time tendency of the *u* and *v* wind components. The mass
continuity term ensures that the temperature and height field are
consistent with the cloud analysis (in a hydrostatic sense), and the
minimization of the *u* and *v* fields ensure a quiet start (i.e.,
minimal gravity wave perturbations due to initial imbalances) for the
NWP model.

Once the final analysis of the mass, momentum, and microphysical species
are produced, a final adjustment to the relative humidity field is made
by saturating any cloudy updrafts. This step was added to account for
the model physics schemes which require saturation for cloud water to
exist. While not necessarily ideal, especially for grids coarser than
those that can resolve clouds, tests indicate that this approach
provides additional improvement to the short-range (0-3 h) forecasts of
clouds and precipitation without adversely impacting the later hours of
the forecast.

**2. FORECAST MODEL SETUP**

Initial testing of the LAPS explicit cloud and precipitation
initialization method has been done using version 3 of the NCAR/PSU
Mesoscale Model 5 (MM5) with slight modifications to the preprocessing
programs and the model initialization routines to account for the
presence of the hydrometeorological species in the initial conditions.

FSL has been running a domain matching the LAPS domain shown in Fig.1 in
real time since the fall of 2000 using the diabatic initialization
procedure described above. The model is run four times daily to produce
24-h forecasts with hourly output. The model is run on FSL\'s
high-performance computing system and is typically available on the
Internet and to the Boulder NWS forecasters within 2 h after the model
initial time, so the quality of the early forecast hours is of
significance for their use.

The model domain uses a grid spacing of 10 km and consists of 125 by 105
points with 41 vertical levels. Vertical grid spacing is finest in the
lower levels to ensure the resolution in the boundary layer is adequate
for the use of the Blackadar PBL scheme. For the microphysical
processes, the Schultz (1995) explicit scheme is used along with the
Kain-Fritsch convective parameterization.

**3. VERIFICATION EXPERIMENT**

To determine what improvement, if any, is gained in forecast quality
using the diabatic initialization, three model forecasts per 6-hourly
cycle were run from October 2000 through January 2001. The primary run,
MM5HOT, was configured as discussed above, using the LAPS diabatic
initialization for initial conditions and the NCEP operational Eta (on
the AWIPS 40-km grid) as the lateral boundary conditions (LBCs). The Eta
run used for LBCs was the 6-hourly cycle prior to the MM5 cycle (e.g.,
the 1200 UTC MM5 run used the 0600 UTC Eta run). The remaining two runs,
MM5ETA and MM5WARM, were configured identically, including the LBCs, but
were initialized differently. The MM5ETA used no LAPS analysis, but
rather was simply initialized from a 6-h Eta forecast (state variables
only). This method is typical of many real-time mesoscale models, and
relies solely upon the higher-resolution terrain to improve upon the
larger scale forecast provided by the regional model. The MM5WARM used a
3-h preforecast analysis nudging period (state-variables only) to
attempt to reduce the model spin-up time. This method has been used
operationally at the Air Force Weather Agency as part of their LAPS and
MM5 implementation. While it does improve the forecast of clouds and
precipitation during the early hours of the forecast, it has the
disadvantage of requiring additional computational time. Table 1
summarizes the three configurations.

  -----------------------------------------------------------------------
  CASE                   IC                       LBC
  ---------------------- ------------------------ -----------------------
  MM5HOT                 LAPS Diabatic            6-30 H Eta Forecast
                         Initialization           

  MM5WARM                LAPS 3-H Nudging Period  6-30 H Eta Forecast

  MM5ETA                 6-H Eta Forecast         6-30 H Eta Forecast
  -----------------------------------------------------------------------

**Table 1.** Initial conditions (IC) and lateral boundary conditions
(LBC) used for the three members of the validation experiment.

For each model cycle, each of these three configurations was verified
against LAPS gridded analyses. Note that the same LAPS analysis was used
to verify all three MM5 runs, and that these LAPS analyses used the NCEP
operational RUC as their first guess to ensure their independence. Only
the first 12 h of each run were verified. Basic error statistics (RMSE
and bias) were computed for the forecast surface temperature, relative
humidity, and sea-level pressure. Additionally, to determine relative
improvements to quantitative precipitation forecasts (QPFs) and cloud
forecasts due to the different initialization techniques, several
different skill scores were computed, including probability of detection
(POD), false alarm rate (FAR), equitable threat score (ETS), bias, and
correlation coefficient. The skill scores were computed by comparing the
analysis and forecast fields, which are on identical grids, on a
gridpoint by gridpoint basis; i.e. the scores are not made more
optimistic by using multiple analysis points for a single forecast
point. Thus, slight timing or spatial errors (1 h or 1 gridpoint error
in the forecast) will negatively impact these scores. The skill scores
were computed as follows:

![](media/image2.emf){width="0.8763888888888889in"
height="0.21388888888888888in"}

![](media/image3.emf){width="0.8361111111111111in"
height="0.21388888888888888in"}

![](media/image4.emf){width="2.8965277777777776in"
height="0.2222222222222222in"}

where *F* is the total number of points where the forecast exceeds the
threshold, *O* is the total number of points observed to exceed the
threshold, *C* is the total points where the threshold was both observed
and forecast (successful forecasts), and *B* is the total number of
points where the threshold was forecast but not observed (false alarms).
In the computation of ETS, *T* is the total number of points in the
horizontal domain, and the *E* term represents the chance of the event
occurring anywhere on the domain, based on the product of the total
number of points forecast and the total number of points observed.

**4. RESULTS**

![](media/image5.emf){width="6.499305555555556in"
height="2.790277777777778in"}![](media/image6.emf){width="6.466666666666667in"
height="2.8875in"}

Both of these graphs visually demonstrate the improvement in the
short-range forecast of clouds and precipitation due to the diabatic
initialization technique. Additionally, both the MM5HOT and MM5WARM
values demonstrate that skill is improved during the early hours when
additional data are assimilated using an analysis system rather than
using a simple \"nestdown\" from a regional model (in this case, the
operational Eta model). Furthermore, the model spin-up time for this
particular domain can be inferred from the graphs by noting the length
of the period between the initial time and the time that the MM5ETA
skill is statistically comparable to the MM5HOT and MM5WARM cases. For
our domain, this appears to be on the order of 5 or 6 h. Since these
grids are available to the forecasters within 2 h of model
initialization time, the additional skill provided by the diabatic
initialization would be beneficial, particularly for automated forecast
systems which use NWP grids as their input.

Finally, the convergence of model skill in the later hours demonstrates
the dominance of the lateral boundary conditions on the resulting
forecast for this limited domain, as well as the inherent low
predictability with time of small-scale meteorological features. We
speculate that the use of a land surface model component in the forecast
model combined with a rapid data assimilation cycle may lengthen the
time period prior to this convergence due to the earlier spin-up of
precipitation processes (and thereby changes to the soil moisture field)
using the diabatic initialization compared to the simple nestdown
technique. While the MM5WARM has comparable skill scores at an earlier
time, the diabatic initialization has the advantage of not requiring the
additional computations of a preforecast analysis nudging period.

**5. CONCLUSIONS AND FUTURE WORK**

This work is still in progress, but shows potential for improving
short-range forecasts of clouds and precipitation without the use of
computationally expensive dynamic initialization periods or the use of
3D/4DVAR data assimilation systems used at the national centers for
medium range and longer forecasts. For local users with limited
computing resources, this system could provide a means for explicit
forecasts of short range weather conditions by taking advantage of local
data sources. Such a system running in real-time within a local forecast
office could greatly complement the regional and national scale products
provided by centralized facilities.

Future work is primarily focused on the following areas:

-   Improving the LAPS dynamic balance scheme by adding thermodynamic
    terms to the cost function equation such that anomalous clouds and
    precipitation in the first guess field can be gracefully removed
    based on the LAPS cloud analysis.

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-   Improving the forecast model microphysical scheme to account for
    fractional cloud cover (i.e., the existence of cloud water in
    non-saturated grid boxes).

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-   Improving grid-scale dependency when assigning vertical motions
    based on derived cloud type.

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-   Computing first-guess background errors for use in the full 3D error
    matrices in the balance package cost function.

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-   Testing the scheme with other NWP models, including the new Weather
    Research and Forecast (WRF) model.

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-   Improving verification, to include point verification against
    observations.

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-   Implementing full 4DDA data assimilation system running on an hourly
    cycle to improve the first guess used by the LAPS analysis.

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-   Running the system at cloud-resolving resolution (e.g., 1-3 km
    horizontal grid spacing).

Additionally, this version of LAPS and MM5 will be installed at three
NWS WFOs and the two USAF space launch facilities for operational use.
This will provide additional opportunities for operational feedback.

**6. References**

Albers, S., J. McGinley, D. Birkenheuer, and J. Smart, 1996: The Local
Analysis and Prediction System (LAPS): Analysis of clouds,
precipitation, and temperature. *Wea. and Forecast.,* **11**, 273-287.

Cram, J. S., S. Albers, M. Jackson, and J. Smart, 1995: Three recent
moisture-related analyses and modeling studies in LAPS. *WMO Itnl.
Workshop on Imbalances of Slowly Varying Components of Predictable
Atmospheric Motions,* Beijing, China, World Meteor. Org., WMO/TD-No.
652, 23-28.

McGinley, J. A., and J. R. Smart, 2001: On providing a cloud-balanced
initial condition for diabatic initialization. *14^th^ Conf. on
Numerical Weather Prediction,* Fort Lauderdale, FL, Amer. Meteor. Soc.,
\[this volume\].

Schultz, P., 1995: An explict cloud physics parameterization for
operational numerical weather prediction. *Monthly Weather Review*,
**123**, 3331-3343.

Schultz, P., and S. Albers, 2001: The use of three-dimensional analyses
of cloud attributes for diabatic initialization of mesoscale models.
*14^th^ Conf. on Numerical Weather Prediction,* Fort Lauderdale, FL,
Amer. Meteor. Soc., \[this volume\].

Shaw, B. L., E. Thaler,and E. Szoke 2001: Operational evaluation of the
LAPS-MM5 \"hot start\" local forecast model. *18^th^ Conf. on Wea. Anal.
and Fcst.,* Fort Lauderdale, FL, Amer. Meteor. Soc., \[this volume\].