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Numerical Weather Prediction and Data Assimilation. David Schultz, Mohan Ramamurthy, Erik Gregow, John Horel. What is a model?. Resource: Kalnay, E., 2003: Atmospheric Modeling, Data Assimilation and Predictability

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Numerical weather prediction and data assimilation

Numerical Weather Prediction and Data Assimilation

David Schultz, Mohan Ramamurthy, Erik Gregow, John Horel


What is a model
What is a model?

  • Resource: Kalnay, E., 2003: Atmospheric Modeling, Data Assimilation and Predictability

  • model: tool for simulating or predicting the behavior of a dynamical system such as the atmosphere

  • Types of models include:

    • heuristic: rule of thumb based on experience or common sense

    • empirical: prediction based on past behavior

    • conceptual: framework for understanding physical processes based on physical reasoning

    • analytic: exact solution to “simplified” equations that describe the dynamical system

    • numerical: integration of governing equations by numerical methods subject to specified initial and boundary conditions


What is numerical weather prediction
What is Numerical Weather Prediction?

  • The technique used to obtain an objective forecast of the future weather (up to possibly two weeks) by solving a set of governing equations that describe the evolution of variables that define the present state of the atmosphere.

  • Feasible only using computers


A brief history
A Brief History

  • Recognition by V. Bjerknes in 1904 that forecasting is fundamentally an initial-value problem and basic system of equations already known

  • L. F. Richardson’s (1922) attempt at practical NWP

  • Radiosonde invention in 1930s made upper-air data available

  • Late 1940s: First successful dynamical-numerical forecast made by Charney, Fjortoft, and von Neumann

  • 1960s: Edward Lorenz shows the atmosphere is chaotic and its predictibility limit is about two weeks


Nwp system
NWP System

  • NWP entails not just the design and development of atmospheric models, but includes all the different components of an NWP system

  • It is an integrated, end-to-end forecast process system



Components of an nwp model
Components of an NWP model

  • 1. Governing equations

    • F=ma, conservation of mass, moisture, and thermodynamic eqn., gas law

  • 2. Numerical procedures:

    • approximations used to estimate each term (especially important for advection terms)

    • approximations used to integrate model forward in time

    • boundary conditions

  • 3. Approximations of physical processes (parameterizations)

  • 4. Initial conditions:

    • Observing systems, objective analysis, initialization, and data assimilation


Model physics
Model Physics

  • Grid-scale precip. (large scale condensation)

  • Deep and shallow convection

  • Microphysics (increasingly becoming important)

  • Evaporation

  • PBL processes, including turbulence

  • Radiation

  • Cloud-radiation interaction

  • Diffusion

  • Gravity wave drag

  • Chemistry (e.g., ozone, aerosols)



Good numerical forecasts require
Good Numerical Forecasts Require… you can simulate with the model.

  • Initial conditions that adequately represent the state of the atmosphere (three-dimensional wind, temperature, pressure, moisture and cloud parameters)

  • Numerical weather prediction model that adequately represents the physical laws of the atmosphere over the whole globe


Sources of error in nwp
Sources of error in NWP you can simulate with the model.

  • Errors in the initial conditions

  • Errors in the model

  • Intrinsic predictability limitations

  • Errors can be random and/or systematic errors


Sources of errors continued

Initial Condition Errors you can simulate with the model.

Observational Data Coverage

Spatial Density

Temporal Frequency

Errors in the Data

Instrument Errors

Representativeness Errors

Errors in Quality Control

Errors in Objective Analysis

Errors in Data Assimilation

Missing Variables

Model Errors

Equations of Motion Incomplete

Errors in Numerical Approximations

Horizontal Resolution

Vertical Resolution

Time Integration Procedure

Boundary Conditions

Horizontal

Vertical

Terrain

Physical Processes

Sources of Errors - continued

Source: Fred Carr


Numerical weather prediction and data assimilation
Given all these assumptions and limitations, you can simulate with the model.we have no right to do as well in forecasting the weather as we do!

Dave sez:

  • What other disciplines forecast the future with as much success as meteorology?


Nwp in finland
NWP in Finland you can simulate with the model.

  • Currently, NWP models are run by FMI (limited domain over Europe) and by the European Centre for Medium-Range Weather Forecasts (global)

  • Currently the FMI model is run at about 9 & 22 km and the ECMWF model is run at 25 km grid spacing, meaning that these models can resolve features about 6 times those grid spacings.

  • The new AROME experimental model is running at 2.5 km grid spacing.


Numerical weather prediction and data assimilation

22 km HIRLAM you can simulate with the model.

9 km HIRLAM

2.5 km AROME


Numerical weather prediction and data assimilation

9 km HIRLAM you can simulate with the model.

2.5 km AROME

observed radar reflectivity


Numerical weather prediction and data assimilation

9 km HIRLAM you can simulate with the model.

2.5 km AROME


The hopes of the testbed
The Hopes of the Testbed you can simulate with the model.

  • Higher-resolution observations will provide higher-resolution initial conditions, which could be put into a higher-resolution NWP model, producing higher-resolution forecasts.

  • The hope is that precise forecasts of convection, the sea breeze, rain/snow forecasting, and winds could be made up to a few hours in advance.

  • BUT…


Difficulties lie ahead
Difficulties Lie Ahead… you can simulate with the model.

  • The reality is often that you end up with a higher-resolution, less-accurate forecast.

  • Results from forecasting/research experiments at the NOAA/Storm Prediction Center show value can be added sometimes with high-resolution forecasts.

  • When that value can be added is a very important forecasting/research question!!!


Difficulties lie ahead1
Difficulties Lie Ahead… you can simulate with the model.

  • Producing the initial conditions from sparse resolution (in space and time) and incomplete observations is not easy.

  • Creating a gridded 3-D/4-D dataset suitable for initializing a NWP model is called data assimilation.

  • How it is proposed to be done in the Helsinki Testbed is described next…


Numerical weather prediction and data assimilation

Erik Gregow Project Manager LAPS you can simulate with the model.


Numerical weather prediction and data assimilation

  • Numerical weather prediction model that adequately represents the physical laws of the atmosphere over the whole globe

  • Initial conditions that adequately represent the state of the atmosphere (three-dimensional wind, temperature, pressure, moisture and cloud parameters)


Good numerical forecasts require1
Good Numerical Forecasts Require… represents the physical laws of the atmosphere over the whole globe

  • Numerical weather prediction model that adequately represents the physical laws of the atmosphere over the whole globe

  • Initial conditions that adequately represent the state of the atmosphere (three-dimensional wind, temperature, pressure, moisture and cloud parameters)


Numerical weather prediction and data assimilation

Monitoring represents the physical laws of the atmosphere over the whole globe

Current

Conditions

September 6

20GMT

A

D

A

S


Potential discussion points
Potential Discussion Points represents the physical laws of the atmosphere over the whole globe

  • Why are analyses needed?

    • Application driven: data assimilation for NWP (forecasting) vs. objective analysis (specifying the present, or past)

  • What are the goals of the analysis?

    • Define microclimates?

      • Requires attention to details of geospatial information (e.g., limit terrain smoothing)

    • Resolve mesoscale/synoptic-scale weather features?

      • Requires good prediction from previous analysis

  • What’s the current state-of-the-art and what’s likely to be available in the future?

    • Deterministic analyses relative to ensembles of analyses (“ensemble synoptic analysis”–Greg Hakim)

  • How is analysis quality determined? What is truth?

    • Why not rely on observations alone to verify model guidance?


Observations vs truth
Observations vs. Truth represents the physical laws of the atmosphere over the whole globe

  • “Truth? You can’t handle the truth!”

  • Truth is unknown and depends on application: “expected value for 5 x 5 km2 area”

  • Assumption: average of many unbiased observations should be same as expected value of truth

  • However, accurate observations may be biased or unrepresentative due to siting or other factors


What s an appropriate analysis given the inequitable distribution of observations

x represents the physical laws of the atmosphere over the whole globe

What’s an appropriate analysis given the inequitable distribution of observations?

Case 3

Case 2

Case 1

?

?

x

?

x

x

= grid cell

= observation


What s an appropriate analysis given the variety of weather phenomena
What’s an appropriate analysis given the variety of weather phenomena?

Elevated Valley Inversions

Front

?

O

?

O

?

O

O

O

O

z

T


Analyses vs truth
Analyses vs. Truth weather phenomena?

Analysis value = Background value + observation Correction

  • An analysis is more than spatial interpolation

  • A good analysis requires:

    • a good background field supplied by a model forecast

    • observations with sufficient density to resolve critical weather and climate features

    • information on the error characteristics of the observations and background field

    • good techniques (forward observation operators) to transform the background gridded values into pseudo observations

  • Analysis error relative to unknown truth should be smaller than errors of observations and background field

  • Ensemble average of analyses should be closer to truth than single deterministic approach IF the analyses are unbiased


Truth continuum vs discrete

Truth weather phenomena?

Truth = H(Truth)

Truth: Continuum vs. Discrete

Truth is unknown

Truth depends on application

Temperature

Truth

West

East


Discrete analysis error goal of objective analysis minimize error relative to truth not truth

Truth weather phenomena?

Analysis

Error

Analysis

Discrete Analysis ErrorGoal of objective analysis: minimize error relative to Truth not Truth!

Temperature

Truth

West

East


Numerical weather prediction and data assimilation
ADAS weather phenomena?

  • Near-real time surface

  • analysis of T, RH, V

  • (Lazarus et al. 2002 WAF;

  • Myrick et al. 2005 WAF;

  • Myrick & Horel2006 WAF)

  • Analyses on NWS GFE

  • grid at 5 km spacing

  • Background field: RUC

  • Horizontal, vertical & anisotropic weighting


Numerical weather prediction and data assimilation

Description: weather phenomena?

In the following slides, temperature results from LAPS/MM5 analysis are shown.

The objective is to compare a normal MM5 analysis with LAPS/MM5 analysis, also verify against some observations that are not included into the LAPS analysis

Input to LAPS analysis is here:

- MM5 9-km resolution (input to MM5 is ECMWF 0.35 deg)

- 52 surface observations from HTB area


Numerical weather prediction and data assimilation

MM5 analysis: Temperature at 9 m height, with 1 km resolution

The analysis is based on 0.35 degree boundary fields from ECMWF operational analysis.

09 Aug 2005,15 UTC


Numerical weather prediction and data assimilation

* resolution

*

23.7

23.4

23.6

*

22.1

*

23.0

24.3

*

*

26.0

*

25.4

*

25.4

23.5

*

*

20.5

*

20.4

20.2

*

22.0

*

20.9

*

*

20.7

*

MM5 analysis: Temperature at 9 m height, with 1 km resolution

Verification: The figures, within the plot, are measurements from certain stations not included in the LAPS analysis

09 Aug 2005,15 UTC


Numerical weather prediction and data assimilation

* resolution

*

23.7

23.4

23.6

*

22.1

*

23.0

24.3

*

*

26.0

*

25.4

*

25.4

23.5

*

*

20.5

*

20.4

20.2

*

22.0

*

20.9

*

*

20.7

*

LAPS/MM5 analysis: Temperature at 9 m height, with 3 km resolution

Verification: The figures, within the plot, are measurements from certain stations not included in the LAPS analysis

09 Aug 2005,15 UTC


Numerical weather prediction and data assimilation

* resolution

*

23.7

23.4

23.6

*

22.1

*

23.0

24.3

*

*

26.0

*

25.4

*

25.4

23.5

*

*

20.5

*

20.4

20.2

*

22.0

*

20.9

*

*

20.7

*

LAPS/MM5 analysis: Temperature at 9 m height, with 1 km resolution

Verification: The figures, within the plot, are measurements from certain stations not included in the LAPS analysis

09 Aug 2005,15 UTC


Numerical weather prediction and data assimilation

* resolution

*

23.7

23.4

23.6

*

22.1

*

23.0

24.3

*

*

26.0

*

25.4

WHAT IS TRUTH?

*

25.4

23.5

*

*

20.5

*

20.4

20.2

*

22.0

*

20.9

*

*

20.7

*

LAPS/MM5 analysis: Temperature at 9 m height, with 1 km resolution

Verification: The figures, within the plot, are measurements from certain stations which are not included in the LAPS analysis

09 Aug 2005,15 UTC


Data assimilation surprises
Data Assimilation Surprises resolution

  • Torn and Hakim (unpublished) have applied an ensemble Kalman filter for several hurricanes to determine the most sensitive regions for forecasts in the western Pacific Ocean. The largest sensitivities are associated with upper-level troughs upstream of the tropical cyclone. Observation impact calculations indicate that assimilating ~40 key observations can have nearly the same impact on the forecast as assimilating all 12,000 available observations.

  • Sensitivity of the 48 hour forecast of tropical cyclone minimum central pressure to the analysis of 500 hPa geopotential height (colors) for the forecast initialized 12 UTC 19 October 2004. Regions of warm (cold) colors indicate that increasing the analysis of 500 hPa height at that point will increase (decrease) the 48 hour forecast of minimum central pressure. The contours are the ensemble mean analysis of 500 hPa height.


More data assimilation woes
More Data Assimilation Woes resolution

  • Adaptive observations: collecting data where the forecast is most sensitive

  • Sometimes assimilating more data produces a worse forecast (Morss and Emanuel)

  • Heretical thought: What if none of the hundreds of observations from the Helsinki Testbed made any difference to the forecast?


Challenges ahead for testbed laps
Challenges Ahead for Testbed/LAPS resolution

  • The Testbed only samples the lower troposphere at best, not the mid and upper troposphere.

  • Weather phenomena, even adequately sampled by the Testbed data, will move out of the Testbed domain within an hour or two.

  • Weather phenomena inadequately sampled by the Testbed data will move into the domain and screw up your forecast.

  • Predictability of mesoscale weather features is unknown.

  • All of this assumes a perfect model.


Challenges ahead for forecasters
Challenges Ahead for Forecasters resolution

  • Determinism is dead—long live probabilistic forecasting!

  • High-resolution model output cannot be interpreted the same way as a coarser-resolution model output.

  • Forecasters need to be retrained.

  • Communication of high-resolution forecasts to end users is not simple (i.e., you cannot just send raw model output to users and expect them to use it).

  • This ensures jobs for good forecasters in the future.