Meteorology is: - Part Art - Part Science - Part Mythology As Science increases, so must Understanding Models provide Guidance, not Forecasts

Meteorology is:- Part Art

- Part Science- Part Mythology

As Science increases, so must Understanding

Models provide Guidance, not Forecasts

The Data Collection Step

Building a Numerical Weather Prediction System

Global Observing Systems

1200 Z Global Rawinsondes

United States Radar Network

Northern Hemisphere Marine Observations -- 12 Hour Total

1200Z Aircraft Wind/Temperature Reports

0000Z North American Automated Aircraft Reports

Geostationary Satellite Cloud Tracked Winds

1200Z Polar Satellite Temperature/Humidity Sounding Information

Microwave Precipitable Water/Temp/Moisture/Surface Winds

The Data Quality Control Step


The Data Quality Control Process

Data Rejection List - Based on Individual Station History

Gross Error Checks - Based of Acceptable Data Thresholds

Complex Quality Control - Attempts to Correct ErrorsNeighbor Checks - Comparison with Nearby Values

Temporal Checks - Assure Consistency Over Time

Temporal Checks - Also Help Resolve Neighbor Checks

People Still Need to Correct the Most Difficult Problems

The Data Analysis Step


The Analysis Problem - Depicting Data as Continuous Fields

Manual Analysis Produces Continuous Images

Objective Analyses InterpolateRandom Data to Regular Grids

Most Areal Weighting Procedures based on Distance and other factors

Basic Analysis Equation

We need Full Analysis Coverage, even over data sparse areas.NWP models move (advect) information from data rich into data sparse regions

Why use a Background “First Guess” field? Can’t we Analyze the Data Directly?

Performing an Objective Analysis

The Analysis begins with a Earlier Forecast as “First Guess”

“First Guess” values are calculated at Observation Points

The Objective Analysis Uses Differences CalculatedBetween the Data and the First Guess -

The Analysis “corrects” the First Guess

The Differences are Interpolated to the Regular Grid

Difference field shows errors in “First Guess Memory”, which the analysis then corrects.

For the analysis to be effective, the errors should be small!

Combining the “First Guess” and Differences at Grid Points Yields the Final Grid Point Values Which shows changes

beyond areas covered by Data

The Big Question becomes:How to determine the Optimal Analysis

Analysis weights

Older Optimal Interpolation schemes took into account:Older Optimal Interpolation schemes took into account:

Station ProximityStation Proximity

Observational Error EstimatesObservational Error Estimates

““First Guess” Error EstimatesFirst Guess” Error Estimates

Statistical/Dynamical Variable CorrelationStatistical/Dynamical Variable Correlation

Data Assimilation - What is it?

In Data Sparse or Complex Areas,In Data Sparse or Complex Areas,

The Analysis is only as Good as The GuessThe Analysis is only as Good as The Guess

To improve the Analysis First Guess,To improve the Analysis First Guess,

Need to include as much Need to include as much DataData as possible into the as possible into the Model at Model at allall times to be able to producing the times to be able to producing the

Best First-Guess Possible for future analyses Best First-Guess Possible for future analyses

Analyses without Data-Assimilation used longer-range forecast ‘First Guess” fields

- 12 to 24 hour forecasts were often used -

Intermediate-time data were ignored and large corrections were made to the model “First-Guess” fields, creating imbalances

Data assimilation systems make a series of smaller changes to shorter-range “First-Guess” forecast fields

Use of 3-hourly analysis steps allows much more data to be used.

Forecasts using data assimilation based analyses preserve details in data better and require less time to become “dynamically active.”

Variational Analysis Techniques represented a major improvement in data assimilation.

In the variational approach, analyses are made of the observed variables in their original form, rather than making

all observations “look like” radiosonde data.

For example, instead of analyzing temperature “soundings” determined from satellite radiances, the model “first guess”

fields are converted to radiances and then the differences between the “first-guess” and observed radiances are

analyzed and converted back to model forecast variables-

This eliminates much “variable conversion error”

Variational Analysis represented a major improvement in data assimilation.

An example of Previous Analysis Approachs:

1. Satellite radiances are observed over continuous areas(Area and Layer average data)

2. Representative “samples” are extracted to reduce data set size

3. Observations are “adjusted” to remove cloud contamination

4. Use radiative transfer laws are used to obtain layer-average temps.

5. Layer-averages are interpolated to standard rawinsonde levels

6. Model Guess is interpolated to sounding levels (x,y,z)

7. Differences are made between Guess and Satellite “data”

8. Differences Interpolated vertically to model levels

9. Differences Interpolated horizontall to grid to get continuous field

Variational Analysis represented a major improvement in data assimilation.

Variational Approach:

1. Radiation laws used to convert temperatures and humidity data taken directly from the native vertical coordinate model to radiances

in layers corresponding to satellite observations, including model cloud information

2. Model-based radiance are interpolated to all satellite observation locations

3. Difference between ‘First Guess” and observations calculated asin radiances

4. Radiance differences interpolated back to model grid along with all other data - including confidence in each data type

5. Radiation laws used to convert results back to temperature/humidity

Variational Analysis Techniques represented a major improvement in data assimilation.

Although the computational techniques in variational analysis are much more complex than basic circular search

procedures, the technique:

1. Eliminates much “variable conversion” error

2. Retains the maximum information from every data set

3. Allows a mixing of many “partial” observation - e.g., combine aircraft winds with 88-D radial velocity component

4. Is no more expensive to run

5. Improves forecast skill notably

This approach is still not the complete answer

Although Data Assimilation systems have Improved the “First Guess’ Fields used for Analyses, the “Direction” of the

“First Guess” forecast does not usually fit the data at intermediate times as well as it could

Correction to “First Guess”

Continuous Assimilation (called 4-D Variational Analysis)

Allows the analysis to create the best possible fit to the total set of observations throughout the entire assimilation period and their changes throughout the entire analysis period by repeatedly running the model both forward and backward – effectively making correction to many

“First Guess” fields. The technique, however, needs many more computer resources

Correction to“Initial”

“First Guess”

Correction to“Second”

“First Guess”

The Forecast Model


Forecast Models have 2 Essential Computational Components

The Model The Model DynamicsDynamics

-- The Equations of Motion-- The Equations of Motion

The Model The Model PhysicsPhysics

-- Physically forced Processes-- Physically forced Processes

Many occur at small scales and must Many occur at small scales and must have their have their effectseffects approximated approximated

The Model Dynamics

Equations of Motion determine how Equations of Motion determine how parcels of air move in response to parcels of air move in response to

accelerations produced by accelerations produced by imbalances between atmospheric imbalances between atmospheric

forces-Coriolis,Pressure Gradient...forces-Coriolis,Pressure Gradient...

Solar Heating in the tropics forces the Global General Circulation by creating pressure gradient forces

Equations of Motion in Simplest Form -- [Horizontal Winds]Parcel Acceleration (total derivative) = Sum of Coriolis and

Pressure Gradient Forces

However, because NWP forecasts are made at Points, not

for Parcels

Therefore, Equations need to be written in terms of Local Wind Changes

These are the Wind Prediction Equations

Note the addition of ‘non-linear’ terms: - Changes in the u wind component are dependent both on the u wind

itself and gradients of the u and v winds,- Likewise for the v wind component

This can lead to numerical instability in grid point models if time computer time steps are too long – needs accurate gradient calculations

For Hydrostatic models, we also must assure Hydrostatic Balance and Mass Continuity

[Continuity used to diagnose Vertical Motion]

Finally, we add Heat and Moisture Conservation -- [Temperature and Moisture]

The Combination of these 6 equations is the basis for all Hydrostatic Weather Prediction Models

Forecasts are produced by solving these Differential Equations using relatively simple arithmetic approximations

to forecast Winds, Temperature, Humidity and Pressure

UUFutureFuture = U = UNow Now + Acceleration * Time Step + Acceleration * Time Step

Models really forecasting for “Boxes” of Air

The Forecast “Box” size depends on the forecast Grid size

Regional Model Grids can be finer than Global Grids and Boundary Conditions from Global Models

Grid resolution limits the types Phenomena that can be forecastedsince a minimum of 5-9 grids points are needed to define and retain

a feature (wave) in the forecast

Most Operational Models use Terrain Following (Sigma) Vertical Coordinate Systems

Most Global Models use “spectral” instead of “grid” for Dynamics Formulations to increase resolution and save time

But, Spectral Models still calculate “physics” on gridsBut, Spectral Models still calculate “physics” on grids

In Spectral Models, Atmospheric waves divided into many wave components and forecast are made for each wave.

Limits truncation errors because derivatives are known precisely (Derivative of Sine is Cosine, etc)

High-Resolution Non-Hydrostatic Models include much more realism - especially for Convectively Driven Phenomena - by

including vertical as well as horizontal accelerations But at a high cost in Model Complexity and required resolution

Forecast Models have 2 Essential Computational Components

The Model The Model DynamicsDynamics

-- The Equations of Motion ---- The Equations of Motion --

Models do this part quite wellModels do this part quite well

- - - - - - - - - - - - -- - - - - - - - - - - - -

The Model The Model PhysicsPhysics

-- Physically forced Processes-- Physically forced Processes

Many occur at small scales and must Many occur at small scales and must have their have their effectseffects approximated approximated

The Model Physics

The Physics Part of Forecast Models The Physics Part of Forecast Models approximates the approximates the EffectsEffects of major of major

complex complex Physical ProcessesPhysical Processes occuring in the atmosphere - often occuring in the atmosphere - often

at smaller scales than can be at smaller scales than can be modeled directly , for example modeled directly , for example

Radiation, Precipitation, Turbulent Radiation, Precipitation, Turbulent Mixing, Friction, ...Mixing, Friction, ...

Radiation Parameterizations

Simplified solutions to the full radiative Simplified solutions to the full radiative transfer process.transfer process.

Often calculated in models over several time Often calculated in models over several time steps.steps.

Highly interactive with other model Highly interactive with other model conditions - namely:conditions - namely:

CloudinessCloudiness

Soil conditionSoil condition

VegetationVegetation

Snow cover Snow cover

Accurate Radiation requires detailed boundary layer information

How does this affect a sounding forecast?

* Surface temperature change depends on incoming sunlight, * Surface temperature change depends on incoming sunlight, surface conditions, advection, low-level lapse rate.surface conditions, advection, low-level lapse rate.

* Incoming short-wave radiation (sunlight) affected by * Incoming short-wave radiation (sunlight) affected by “cloudiness” in model, determined either from mean relative “cloudiness” in model, determined either from mean relative humidity or average liquid water content in 3-5 layers of the humidity or average liquid water content in 3-5 layers of the

atmosphere.atmosphere.

* Vertical mixing of temperature, moisture and momentum * Vertical mixing of temperature, moisture and momentum (winds) affected by surface temperature, boundary layer (winds) affected by surface temperature, boundary layer

lapse rate and boundary layer wind shear - often based on lapse rate and boundary layer wind shear - often based on combination of theoretical rules and observational studies. combination of theoretical rules and observational studies.

Compound effects of radiation apparent in forecast soundings

By 9:00 AM Local, Surface is heating - Wind increasing

By Local Noon, Boundary Layer well mixed

By mid-afternoon, Momemtum mixes downward, transporting momentum and producing surface wind “gusts”

By Evening, Cooling separates Surface from Boundary Layer

On a clear, dry evening, the problem is simply one of calculating the temperature change due to outgoing longwave radiation, which itself

depends on the surface temperature

When a layer of the atmosphere is humid or partly cloudy, only part of the longwave radiation goes directly to space, the remainder is absorbed and re-emitted to space and earth, usually at a lower rate

On a clear day, solar energy is partially reflected back to space and partially absorbed by the earth’s surface. The surface then re-radiates some longwave radiation to space and conducts other to the soil below

and air above - part of which may be convected (mixed) to higher levels.

If the surface is light colored, more solar radiation is reflected and less soil and atmospheric heating is predicted. For this to be predicted

well, the model must have proper information on soil type.

As the cloud layer thickens, more solar radiation is reflected. For this to be predicted properly, the cloud layers must be relatively thick.

The amount of reflected sunlight also varies with time of day over oceans. Here, however, the radiation that is absorbed by the oceans

surface is transported both upwards and downwards by convection in the atmosphere and the oceans.

If a lower atmospheric layers are moist, some of the incoming solar radiation will be scattered upward and downward, reducing the net

incoming radiation and surface heating. Likewise, outgoing longwave radiation will be absorbed and retransmitted upward and downward.

Over dry soil, most of the solar radiation heats the soil, which in turn heats the lowest part of the atmosphere. This heat is then mixed

upwards to heat successively deeper parts of the boundary layer. Heat also goes into the soil layers (another parameterized process)

Where the soil is moist, the solar radiation both heats and moistens the boundary layer, with evaporation reducing surface heating. Incorrect surface moisture in forecast models can cause local temperature and

precipitation errors - depending on the area and degree of soil saturation

Most models also include information about vegetation type and seasonal growth rate. In areas of active plant growth, radiation is

absorbed by the plants. Part is assumed to support the plant growth, while the remainder heats and moistens the boundary layer.

Soil models allow plants to move water from below the earth’s surface to the atmosphere through the process of evapo-transporation. This requires information about plant type and growth rate, soil type and

past precipitation, . . . .

Surface Temperature forecast with Correct Snow CoverAll Precipitation along US East Coast was forecast as rain

Forecast with Incorrect Snow cover was for Snow and Freezing Rain forecast along US East Coast

Boundary Layer Temperature Errors exceeded 6C

Warning: The effects of Physical Parameterizations can produce misleadingly detailed dynamical responses, due to

horizontal models resolution limits

Inland extent of Sea Breeze Inland extent of Sea Breeze depends on model grid spacingdepends on model grid spacing

Realism of Precipitation and its effects limited by grid resolution - and represent areal averages

NWP Models must be able to simulate NWP Models must be able to simulate many different types of clouds and precipitationmany different types of clouds and precipitation

Precipitation Parameterization

Precipitation divided into two types in model:Precipitation divided into two types in model: ““Large Scale” or “Grid Scale” PrecipitationLarge Scale” or “Grid Scale” Precipitation - Which is - Which is

used to simulate the occurance of Stratiform used to simulate the occurance of Stratiform PrecipitationPrecipitation

““Convective” - which is used to account for the effects Convective” - which is used to account for the effects of deep convection on heating and moistening the of deep convection on heating and moistening the atmosphereatmosphere

Not having a separation allows too much latent heating Not having a separation allows too much latent heating to concentrate at low levels in the atmosphere and to concentrate at low levels in the atmosphere and produce overdevelopment.produce overdevelopment.

“Grid Scale” Precipitation

The Forecast models essentially emulate what you, The Forecast models essentially emulate what you, as forecasters, do using a thermodynamic diagram. as forecasters, do using a thermodynamic diagram. Air is lifted by the vertical motions predicted in the Air is lifted by the vertical motions predicted in the model. When a Relative Humidity saturation limit model. When a Relative Humidity saturation limit is reached (usually between 90-98%), precipitation is reached (usually between 90-98%), precipitation forms. It falls to the ground, with some evaporating forms. It falls to the ground, with some evaporating

and moistening the lower model layers.and moistening the lower model layers.

Some models today are beginning to forecast Some models today are beginning to forecast Liquid Liquid Cloud Water,Cloud Water, in which case when saturation occurs, in which case when saturation occurs, clouds are formed first, followed by precipitation. clouds are formed first, followed by precipitation.

- Grid-scale precipitation and cloud parameterization (PCP) are model - Grid-scale precipitation and cloud parameterization (PCP) are model emulations of cloud and precipitation processes that remove excess atmospheric emulations of cloud and precipitation processes that remove excess atmospheric

moisture directly resulting from the dynamically driven forecast wind, moisture directly resulting from the dynamically driven forecast wind, temperature, and moisture fields. temperature, and moisture fields.

- While grid-scale motions determine the forcing, additional cloud and - While grid-scale motions determine the forcing, additional cloud and precipitation processes occurring at scales much smaller than a grid box also precipitation processes occurring at scales much smaller than a grid box also

influence the true microphysical response and must be parameterized.influence the true microphysical response and must be parameterized.

- Development of clouds and precipitation in the PCP scheme results in latent heating - Development of clouds and precipitation in the PCP scheme results in latent heating from condensation (indicated by the red area in the animation), which changes the from condensation (indicated by the red area in the animation), which changes the

wind, temperature, and moisture fields. wind, temperature, and moisture fields. - Evaporative cooling of the air from falling precipitation takes place in subsaturated - Evaporative cooling of the air from falling precipitation takes place in subsaturated

layers below where precipitation is formed (the blue area in the animation). layers below where precipitation is formed (the blue area in the animation). - Over time, these - Over time, these feedbacksfeedbacks onto model forecast variables may further strengthen the onto model forecast variables may further strengthen the circulation that initially produced the model clouds and precipitation. The strengthened circulation that initially produced the model clouds and precipitation. The strengthened circulation may increase the precipitation and latent heating, which, in turn, may result circulation may increase the precipitation and latent heating, which, in turn, may result

in additional feedbacks. in additional feedbacks.

Grid-scale precipitation and cloud parameterization (PCP) are model emulations of Grid-scale precipitation and cloud parameterization (PCP) are model emulations of cloud and precipitation processes that remove excess atmospheric moisture directly cloud and precipitation processes that remove excess atmospheric moisture directly resulting from the dynamically driven forecast wind, temperature, and moisture fields. resulting from the dynamically driven forecast wind, temperature, and moisture fields.

- Traditionally, PCP schemes had infer the presence of clouds in grid layers based upon - Traditionally, PCP schemes had infer the presence of clouds in grid layers based upon RH saturation thresholds and RH saturation thresholds and immediatelyimmediately condense all excess moisture into condense all excess moisture into

precipitation. Most schemes originally used 100% RH as the critical RH saturation precipitation. Most schemes originally used 100% RH as the critical RH saturation threshold, but later used values between 75% and 85% to account for scattered threshold, but later used values between 75% and 85% to account for scattered

precipitation within grid box. precipitation within grid box. - Recent advances in model horizontal/vertical resolution, physics, and computing - Recent advances in model horizontal/vertical resolution, physics, and computing

power have allowed more realistic PCP schemes that include predicted cloud water. power have allowed more realistic PCP schemes that include predicted cloud water. These schemes range from simple schemes that account for cloud water only to more These schemes range from simple schemes that account for cloud water only to more

complex schemes that include many types of hydrometeors and internal cloud processes.complex schemes that include many types of hydrometeors and internal cloud processes.

- Despite the improved simulation of physical processes in PCP schemes, the model's - Despite the improved simulation of physical processes in PCP schemes, the model's large-scale forcing fields continue to play a larger role in determining the amount of large-scale forcing fields continue to play a larger role in determining the amount of

precipitation over a broad region from a given weather system than the degree of detail precipitation over a broad region from a given weather system than the degree of detail in the PCP scheme in the PCP scheme

Using Inferred Clouds: Description, Models, & ProcessUsing Inferred Clouds: Description, Models, & Process

These schemes These schemes inferinfer precipitation to remove excess moisture and infer clouds, all precipitation to remove excess moisture and infer clouds, all based on RH. (Note that this order is not physically correct, since precipitation forms based on RH. (Note that this order is not physically correct, since precipitation forms

from clouds in reality.)from clouds in reality.)

Process of removing grid-scale moisture:Process of removing grid-scale moisture:- Areas of excess moisture or supersaturation must be present in the sounding to - Areas of excess moisture or supersaturation must be present in the sounding to

diagnose precipitation, not including cloud ice or phase changes diagnose precipitation, not including cloud ice or phase changes

Using Inferred Clouds: Description, Models, & ProcessUsing Inferred Clouds: Description, Models, & Process

-In areas of excess moisture or supersaturation, temperatures warm from latent heat In areas of excess moisture or supersaturation, temperatures warm from latent heat release, and the specific humidity and dewpoint decrease as water vapor condenses release, and the specific humidity and dewpoint decrease as water vapor condenses

until the temperature and dewpoint are equaluntil the temperature and dewpoint are equal

-Precipitation falls out Precipitation falls out instantaneouslyinstantaneously. Sub-saturated areas beneath the precipitation . Sub-saturated areas beneath the precipitation production layers are cooled and moistened by the evaporation of some falling production layers are cooled and moistened by the evaporation of some falling

precipitationprecipitation

- All water in the atmosphere remains in vapor form. The resulting RH is too high, - All water in the atmosphere remains in vapor form. The resulting RH is too high, because no water is held in cloudbecause no water is held in cloud

In contrast to schemes using inferred clouds, schemes using predicted clouds follow a In contrast to schemes using inferred clouds, schemes using predicted clouds follow a physically based sequence of forming clouds prior to precipitation. Schemes using physically based sequence of forming clouds prior to precipitation. Schemes using

simple cloudssimple clouds diagnose precipitation from cloud water (or ice) only. diagnose precipitation from cloud water (or ice) only.

Schemes using Schemes using complex cloudscomplex clouds, on the other hand, predict precipitation directly , on the other hand, predict precipitation directly through the modeling of internal cloud processes, including multiple cloud and through the modeling of internal cloud processes, including multiple cloud and

precipitation hydrometeor types.precipitation hydrometeor types.

Schemes Using Simple Clouds: Description, Models, & ProcessSchemes Using Simple Clouds: Description, Models, & Process

Description: Description: These are schemes that predict cloud water/ice based on RH and then These are schemes that predict cloud water/ice based on RH and then infer or diagnose precipitation based on cloud water/ice amountinfer or diagnose precipitation based on cloud water/ice amount

-Uses critical RH level (generally below 100%) to account for sub grid-scale moisture -Uses critical RH level (generally below 100%) to account for sub grid-scale moisture variability in order to account for the amount of cloud water variability in order to account for the amount of cloud water

-Supersaturation is -Supersaturation is notnot required to create cloud liquid and ice required to create cloud liquid and ice -Forms clouds first -Forms clouds first -Accounts for partial cloudiness through overcast cloud cover as RH increases -Accounts for partial cloudiness through overcast cloud cover as RH increases above the critical value above the critical value



-Where cloud water is condensed, latent heat is released and specific humidity is -Where cloud water is condensed, latent heat is released and specific humidity is reduced, warming the temperature and lowering the dewpoint and RH around the cloudreduced, warming the temperature and lowering the dewpoint and RH around the cloud-May include cloud ice and supercooled cloud droplets -May include cloud ice and supercooled cloud droplets

-In sub-freezing cloud layers, the cloud water phase may depend upon -In sub-freezing cloud layers, the cloud water phase may depend upon physical physical parameters, such as cloud top temperature parameters, such as cloud top temperature

-May crudely emulate interactions between supercooled cloud water and ice, -May crudely emulate interactions between supercooled cloud water and ice, thereby accounting for temperature effects on precipitation ratesthereby accounting for temperature effects on precipitation rates



-If the cloud water amount exceeds a critical value, precipitation is created from cloud -If the cloud water amount exceeds a critical value, precipitation is created from cloud water water

-Precipitation may be produced within the cloud from a combination of cloud -Precipitation may be produced within the cloud from a combination of cloud water creation, advection, and, in some more complete PCP schemes, input of water creation, advection, and, in some more complete PCP schemes, input of diagnosed diagnosed convectiveconvective cloud water from the model's CP scheme cloud water from the model's CP scheme



-In areas of excess moisture or supersaturation, temperatures warm from latent heat -In areas of excess moisture or supersaturation, temperatures warm from latent heat release, and the specific humidity and dewpoint decrease as water vapor condenses release, and the specific humidity and dewpoint decrease as water vapor condenses until the temperature and dewpoint are equaluntil the temperature and dewpoint are equal-Precipitation falls out -Precipitation falls out instantaneouslyinstantaneously. Sub-saturated areas beneath the precipitation . Sub-saturated areas beneath the precipitation production layers are cooled and moistened by the evaporation of some precipitationproduction layers are cooled and moistened by the evaporation of some precipitation-The resulting RH is more realistic because some water and ice is condensed in clouds; -The resulting RH is more realistic because some water and ice is condensed in clouds; not all is held in vapor form as in inferred cloud schemesnot all is held in vapor form as in inferred cloud schemes

Schemes Using Simple Clouds - Strengths:Schemes Using Simple Clouds - Strengths:

-Measurable improvement in precipitation amount and location over schemes -Measurable improvement in precipitation amount and location over schemes with inferred cloud because:with inferred cloud because:

-Clouds can be advected,-Clouds can be advected,-The effects of cloud ice on precipitation processes can be accounted -The effects of cloud ice on precipitation processes can be accounted

for, for, which allows more realistic microphysics parameterization ,which allows more realistic microphysics parameterization ,- RH fields are more realistic since some water and/or ice is held in - RH fields are more realistic since some water and/or ice is held in

cloudsclouds-The PCP scheme may have direct interaction with the CP scheme -The PCP scheme may have direct interaction with the CP scheme

through input of convective cloud water through input of convective cloud water

--Allow direct comparisons of model initial and forecast cloud fields with Allow direct comparisons of model initial and forecast cloud fields with satellite imagery satellite imagery

-Allow assimilation of cloud data to improve moisture fields since cloud water is a -Allow assimilation of cloud data to improve moisture fields since cloud water is a predicted variable predicted variable

-Allow direct and consistent linkage between cloud and radiation processes -Allow direct and consistent linkage between cloud and radiation processes

-Distinguishing between cloud water and ice improves the simulation -Distinguishing between cloud water and ice improves the simulation of of radiative effects of water versus ice clouds radiative effects of water versus ice clouds

----Are better suited for higher-resolution models because more microphysics Are better suited for higher-resolution models because more microphysics details and smaller-scale motions can be taken into account details and smaller-scale motions can be taken into account

Schemes Using Simple Clouds – Limitations: Schemes Using Simple Clouds – Limitations:

-More computationally expensive -More computationally expensive

-Improvements in precipitation forecast are not complete because :-Improvements in precipitation forecast are not complete because :-Precipitation is still a byproduct, rather than predicted directly, and -Precipitation is still a byproduct, rather than predicted directly, and falls to the ground in one time stepfalls to the ground in one time step-Important microphysical parameterizations are relatively crude -Important microphysical parameterizations are relatively crude

-Precipitation hydrometeors are not explicitly predicted, which -Precipitation hydrometeors are not explicitly predicted, which affects forecast precipitation location and amount, especially for affects forecast precipitation location and amount, especially for very light and heavy precipitation and where horizontal very light and heavy precipitation and where horizontal advection of precipitation is important (primarily snow) advection of precipitation is important (primarily snow)

-The precipitation rate is an average for a grid box which can lead to:-The precipitation rate is an average for a grid box which can lead to:

-Over or under forecasts of precipitation by the model depending -Over or under forecasts of precipitation by the model depending upon upon the actual extent and rate of the precipitation the actual extent and rate of the precipitation

-In reality, precipitation rates may vary -In reality, precipitation rates may vary considerably at considerably at individual points within a grid-box areaindividual points within a grid-box area

-In reality, sub grid-scale variability in -In reality, sub grid-scale variability in precipitation precipitation amount increases as the grid-box area increasesamount increases as the grid-box area increases

-Microphysics are too simple to be able to predict convective processes, such as the -Microphysics are too simple to be able to predict convective processes, such as the creation of cold pools and gust fronts creation of cold pools and gust fronts

Schemes Using Complex Clouds: Description, Models, & Process Schemes Using Complex Clouds: Description, Models, & Process

Description: These schemes predict clouds and precipitation based on RH by directly Description: These schemes predict clouds and precipitation based on RH by directly predicting precipitation hydrometeors and accounting for internal cloud processes. predicting precipitation hydrometeors and accounting for internal cloud processes.

-These schemes are only used in higher-resolution models because they require -These schemes are only used in higher-resolution models because they require sufficient model resolution to resolve small-scale variability affecting microphysical sufficient model resolution to resolve small-scale variability affecting microphysical processes. processes. -Use critical RH level (generally below 100%) to account for sub grid-scale moisture -Use critical RH level (generally below 100%) to account for sub grid-scale moisture variability and patchy clouds variability and patchy clouds

-Supersaturation is -Supersaturation is notnot required to create cloud liquid and ice required to create cloud liquid and ice-Include multiple internal cloud processes, such as mixed phases and -Include multiple internal cloud processes, such as mixed phases and

graupel graupel



-Where water vapor condenses onto any hydrometeor or becomes cloud liquid or ice, -Where water vapor condenses onto any hydrometeor or becomes cloud liquid or ice, latent heat is released, warming the environmental temperature. latent heat is released, warming the environmental temperature.

-Water vapor is used in the condensation process, reducing the environmental -Water vapor is used in the condensation process, reducing the environmental specific humidityspecific humidity



-As precipitation starts to fall from the cloud:-As precipitation starts to fall from the cloud:

-Cooling and moistening occur near the freezing level from melting and occur -Cooling and moistening occur near the freezing level from melting and occur in the sub-cloud layer from evaporation in the sub-cloud layer from evaporation

-Mixed phase hydrometeor interactions and phase changes can occur-Mixed phase hydrometeor interactions and phase changes can occur



-As precipitation falls from the cloud:-As precipitation falls from the cloud:

-Precipitation is tracked as it falls to the ground, rather than falling to the -Precipitation is tracked as it falls to the ground, rather than falling to the ground instantaneously. ground instantaneously.

-Sub-saturated areas moisten and cool as precipitation falls -Sub-saturated areas moisten and cool as precipitation falls -Some water or ice remains held in clouds, making the resulting environmental -Some water or ice remains held in clouds, making the resulting environmental RH more realisticRH more realistic

“Convective” ParameterizationsDo not forecast Convection directly, but account for its Do not forecast Convection directly, but account for its

effects.effects.

Necessary to transport latent heat and moisture into the Necessary to transport latent heat and moisture into the upper troposphere - especially important in the tropics.upper troposphere - especially important in the tropics.

Intensity and beginning usually related in some way to Intensity and beginning usually related in some way to Moisture Flux ConvergenceMoisture Flux Convergence..

No scheme is perfect - Most “tuned” for tropical oceanic No scheme is perfect - Most “tuned” for tropical oceanic environments and all assumeenvironments and all assume effects will occur over effects will occur over

entire grid box.entire grid box.

Remember: Convective Parameterizations are designed to Remember: Convective Parameterizations are designed to STABILIZE the atmosphere, are ‘intermittent’, and do STABILIZE the atmosphere, are ‘intermittent’, and do

not match observed diurnal cycle in tropicsnot match observed diurnal cycle in tropics

How Convection is Formed in a ModelHow Convection is Formed in a Model What happens when the PCP scheme tries to remove instability by creating "grid-scale" What happens when the PCP scheme tries to remove instability by creating "grid-scale" convection? The results depend largely upon how the scheme redistributes heat and convection? The results depend largely upon how the scheme redistributes heat and moisture when it 'convects,' which, in turn, depends upon the types of motion that the moisture when it 'convects,' which, in turn, depends upon the types of motion that the dynamics in the model are able to forecast.dynamics in the model are able to forecast.

Since model forecast vertical velocities are much smaller than actual convective updraft Since model forecast vertical velocities are much smaller than actual convective updraft velocities, the moisture is carried upward too slowly and the PCP scheme releases latent velocities, the moisture is carried upward too slowly and the PCP scheme releases latent heating over a longer period of time and primarily in the lower troposphereheating over a longer period of time and primarily in the lower troposphere. .

--CP schemes parameterize the vigorous vertical CP schemes parameterize the vigorous vertical transports done by updrafts and downdrafts, which transports done by updrafts and downdrafts, which move diabatically heated lower tropospheric air into move diabatically heated lower tropospheric air into the upper troposphere and evaporatively cooled mid-the upper troposphere and evaporatively cooled mid-tropospheric air to the boundary layer.tropospheric air to the boundary layer.

-Additionally, subsidence warming of the -Additionally, subsidence warming of the environment occurs below the convective cloud tops. environment occurs below the convective cloud tops. A typical example of the net result looks like the A typical example of the net result looks like the blue curve in the graphic, with peak heating in the blue curve in the graphic, with peak heating in the mid to upper levels. mid to upper levels.

Because cyclogenesis tends to occur in the layer where the heating rate increases Because cyclogenesis tends to occur in the layer where the heating rate increases rapidly with height, low-level cyclogenesis ensues if a model tries to create grid-scale rapidly with height, low-level cyclogenesis ensues if a model tries to create grid-scale

convection with the PCP scheme. convection with the PCP scheme. By contrast, when the CP scheme creates convection, the tendency toward cyclogenesis By contrast, when the CP scheme creates convection, the tendency toward cyclogenesis

is at mid levels (for instance, like an MCC vortex) and is much weakeris at mid levels (for instance, like an MCC vortex) and is much weaker

-Additionally, -Additionally, the low-level cyclogenesis occurring the low-level cyclogenesis occurring with PCP scheme grid-scale convection feeds back on with PCP scheme grid-scale convection feeds back on itself by enhancing low-level convergence, which itself by enhancing low-level convergence, which further intensifies the heating and cyclogenesisfurther intensifies the heating and cyclogenesis. .

-If the region of excessive latent heating and -If the region of excessive latent heating and precipitation grows in size, the disturbance created can precipitation grows in size, the disturbance created can become dynamically balanced and long lasting, like a become dynamically balanced and long lasting, like a typical synoptic cyclone, although it may have warm typical synoptic cyclone, although it may have warm core characteristics, especially in the lower core characteristics, especially in the lower troposphere. troposphere.

-Although this has sometimes been referred to as -Although this has sometimes been referred to as "convective feedback,""convective feedback," the forecast errors result from the forecast errors result from what the CP scheme did what the CP scheme did notnot do! do!

Because cyclogenesis tends to occur in the layer where the heating rate increases Because cyclogenesis tends to occur in the layer where the heating rate increases rapidly with height, low-level cyclogenesis ensues if a model tries to create grid-scale rapidly with height, low-level cyclogenesis ensues if a model tries to create grid-scale

convection with the PCP scheme. convection with the PCP scheme. By contrast, when the CP scheme creates convection, the tendency toward cyclogenesis By contrast, when the CP scheme creates convection, the tendency toward cyclogenesis

is at mid levels (for instance, like an MCC vortex) and is much weakeris at mid levels (for instance, like an MCC vortex) and is much weaker

--Models use CP schemes to relieve instability before Models use CP schemes to relieve instability before the PCP scheme tries to form grid-scale convection!the PCP scheme tries to form grid-scale convection!

-Note: If the CP scheme is used but fails -Note: If the CP scheme is used but fails to sufficiently relieve instability, the model to sufficiently relieve instability, the model will still will still make grid-scale convection where make grid-scale convection where there is upward there is upward motion and sufficient motion and sufficient moisturemoisture. .

-As model resolution increases, the area covered by a -As model resolution increases, the area covered by a grid box decreases and the grid-scale vertical grid box decreases and the grid-scale vertical velocities increase, so the heating profile and velocities increase, so the heating profile and consequent forecast impacts become more realistic. consequent forecast impacts become more realistic.

-When grid spacing gets down to 1 or 2 km, -When grid spacing gets down to 1 or 2 km, convection can be simulated directly and no convection can be simulated directly and no CP CP scheme is used.scheme is used.

Role of CP in ModelsRole of CP in Models

-In nature, convection not only produces precipitation, but also transport sheat -In nature, convection not only produces precipitation, but also transport sheat upward, redistributes moisture, and thereby upward, redistributes moisture, and thereby stabilizesstabilizes the atmosphere. the atmosphere.

-If enough convection occurs over a large enough area, it can also create outflow jets -If enough convection occurs over a large enough area, it can also create outflow jets and mid-level vortices and drive larger atmospheric circulations that affect weather and mid-level vortices and drive larger atmospheric circulations that affect weather

in distant locations, etc.in distant locations, etc.


To accomplish both tasks, each scheme must define the following, using information To accomplish both tasks, each scheme must define the following, using information averaged over entire grid boxes: averaged over entire grid boxes: -What triggers convection in a grid column-What triggers convection in a grid column

-How convection, when present, modifies the sounding in the grid -How convection, when present, modifies the sounding in the grid columncolumn -How convection and grid-scale dynamics affect each other -How convection and grid-scale dynamics affect each other


-How a scheme handles these assumptions can limit its effectiveness. -How a scheme handles these assumptions can limit its effectiveness. -Furthermore, the parameters used in the assumptions are adjusted to optimize the -Furthermore, the parameters used in the assumptions are adjusted to optimize the

scheme's overall performance in all situations. scheme's overall performance in all situations. -They may work well for some situations but work poorly for others. -They may work well for some situations but work poorly for others. (They perform well in average situations but break down in extreme events.)(They perform well in average situations but break down in extreme events.)

An example of the Kuo SchemeAn example of the Kuo Scheme

Description: A Description: A simplesimple scheme produces precipitation and increases static stability by scheme produces precipitation and increases static stability by emulating moist-adiabatic ascent –temperature/moisture profiles emulating moist-adiabatic ascent –temperature/moisture profiles moist adiabatic moist adiabatic

Trigger: Convection is triggered by any pre-specified amount of Convective Available Trigger: Convection is triggered by any pre-specified amount of Convective Available Potential Energy (CAPE) and column-integrated moisture convergence exceeding a Potential Energy (CAPE) and column-integrated moisture convergence exceeding a threshold valuethreshold value

Kuo Scheme: Description, Models, & TriggerKuo Scheme: Description, Models, & Trigger


-Moves temperature profile throughout the cloud -Moves temperature profile throughout the cloud towardtoward a low-level moist adiabat. a low-level moist adiabat. -Some of the moisture moistens the sounding while some falls instantly as -Some of the moisture moistens the sounding while some falls instantly as

rain.rain.



-The amount of rain produced varies by model, even for the same conditions. -The amount of rain produced varies by model, even for the same conditions. -The division of the moisture supply into that which moistens -The division of the moisture supply into that which moistens the the

sounding sounding and the part that falls as rain can vary from model to model. and the part that falls as rain can vary from model to model. -If most of the moisture is used for moistening the sounding, the PCP -If most of the moisture is used for moistening the sounding, the PCP

scheme may eventually generate some precipitation. scheme may eventually generate some precipitation.



--Link to large-scale forcing: Link to large-scale forcing: The intensity and continuation of convective The intensity and continuation of convective precipitation and sounding changes depend upon low-level moisture convergenceprecipitation and sounding changes depend upon low-level moisture convergence

-The scheme assumes that convection consumes moisture at the rate supplied -The scheme assumes that convection consumes moisture at the rate supplied by the large-scale wind and moisture fields.by the large-scale wind and moisture fields.



--Final state: Final state: Temperature and sub-saturated moisture profiles Temperature and sub-saturated moisture profiles approach approach moist moist adiabatic, but do not reach them. adiabatic, but do not reach them.

-The scheme assumes that convection does not occupy the entire grid column, -The scheme assumes that convection does not occupy the entire grid column, although it continues to moisten and approach moist adiabatic as convection although it continues to moisten and approach moist adiabatic as convection persists.persists.

Kuo Scheme: Strengths & Limitations Kuo Scheme: Strengths & Limitations

Strengths:Strengths:

-Essence and behavior is easy to understand-Essence and behavior is easy to understand

-Runs quickly; requires few computing resources-Runs quickly; requires few computing resources

Limitations:Limitations:

-Simplistic scheme; cannot represent the variety of things that happen in nature-Simplistic scheme; cannot represent the variety of things that happen in nature

-Does not account for the strength of cap inhibiting convective development-Does not account for the strength of cap inhibiting convective development

-Positive feedback (including precipitation bull's-eyes) sometimes occurs because the -Positive feedback (including precipitation bull's-eyes) sometimes occurs because the model response to parameterized convective heating may generate moisture model response to parameterized convective heating may generate moisture convergence, which triggers the scheme again. This behavior stems from assuming that convergence, which triggers the scheme again. This behavior stems from assuming that moisture convergence causes convectionmoisture convergence causes convection

-Many variations exist (for example, some include downdrafts, while others do not). -Many variations exist (for example, some include downdrafts, while others do not).

-Each formulation results in a variety of unrealistic physical behaviors-Each formulation results in a variety of unrealistic physical behaviors

Many different Convective Parameterizations are used in various modelsMany different Convective Parameterizations are used in various models

Each scheme has it’s own advantages and disadvantagesEach scheme has it’s own advantages and disadvantages

NONE of them simulate CONVECTION, only account for it’s effectsNONE of them simulate CONVECTION, only account for it’s effects

Instead of spending all day talking about this, Instead of spending all day talking about this, Much more information and examples are available in the onlineMuch more information and examples are available in the online

COMET NWP Training ModuleCOMET NWP Training Module

Future Model Trends

Future Weather Forecast Model Future Weather Forecast Model development will be limited by data - development will be limited by data -

both observations and understanding of both observations and understanding of detailed boundary conditions.detailed boundary conditions.

- - - - - - - - - - - - - - - - - - - -

To To SimulateSimulate is is notnot the same as the same as ForecastForecast

- - - - - - - - - -- - - - - - - - - -

Probabilistic forecasts provide not only Probabilistic forecasts provide not only forecast data, but indications of forecast data, but indications of

confidence in the forecastsconfidence in the forecasts

Post-Processing of Forecast Model GuidanceStatistical Methods


Statistical Model Post-processing

Why do it?

* Correct model deficiencies.

Resolution deficiencies (e.g., model grid too course to include sea breeze)

Errors - Models have 2 types of errors:

- Systematic errors (e.g., biases - always too warm at ...)

- Random errors (e.g., wrong one day, good the next)


Why do it?

* Correct model deficiencies - Model Errors: Systematic errors can be corrected - with varying degrees of success.

Random errors can NOT be corrected using known procedure.

Probabilistic forecasting is addressing this


Why do it?

* Correct model deficiencies.

Local climate information can be added to model outputs once a long history of the model performance is known

* Provide forecasts of parameters not directly included in the model (e.g., visibility)

Now the QUIZNow the QUIZ

Rank ( 1 to 10, “well” to “not-well” ) the ability of Rank ( 1 to 10, “well” to “not-well” ) the ability of NWP models to forecasts the following parameters:NWP models to forecasts the following parameters:

__ Temperature__ Temperature__ Height__ Height__ Winds aloft__ Winds aloft__ Moisture__ Moisture__ Cloudiness__ Cloudiness__ Maximum / Minimum Temperature__ Maximum / Minimum Temperature__ Surface Winds__ Surface Winds__ Stratoform Precipitation__ Stratoform Precipitation__ Convective Precipitation__ Convective Precipitation

Now the QUIZNow the QUIZ

Rank ( 1 to 10, “well” to “not-well” ) the ability of Rank ( 1 to 10, “well” to “not-well” ) the ability of NWP models to forecasts the following parameters:NWP models to forecasts the following parameters:

_2_ Temperature_2_ Temperature_1_ Height_1_ Height_3_ Winds aloft_3_ Winds aloft_4_ Moisture_4_ Moisture_7_ Cloudiness_7_ Cloudiness_5_ Maximum / Minimum Temperature_5_ Maximum / Minimum Temperature_6_ Surface Winds_6_ Surface Winds_7_ Stratoform Precipitation_7_ Stratoform Precipitation_10_ Convective Precipitation_10_ Convective Precipitation

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Meteorology is: - Part Art - Part Science - Part Mythology As Science increases, so must Understanding Models provide Guidance, not Forecasts