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Resolving Clouds in Atmospheric Models Bill Skamarock NCAR/MMM Clouds in the Atmosphere Weather: Precipitation – rain, snow, hail Wind, radiation, visibility Chemistry/Air-Quality: Chemical processing (acid rain) Ozone chemistry Transport of pollutants

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clouds in the atmosphere

Clouds in the Atmosphere

Weather:

Precipitation – rain, snow, hail

Wind, radiation, visibility

Chemistry/Air-Quality:

Chemical processing (acid rain)

Ozone chemistry

Transport of pollutants

Wet deposition

Climate

Moisture redistribution and precipitation

– hydrological cycle

Radiation

representation of clouds in atmospheric models
Representation of Clouds in Atmospheric Models

Large-scale models: h > 30 km

  • The effects of the clouds are diagnosed (parameterized) from
  • the predicted water vapor field
  • precipitation
  • vertical transport and redistribution of moisture and heat
  • radiative effects
  • turbulence
representation of clouds in atmospheric models5
Representation of Clouds in Atmospheric Models

Meso-scale models: 8 km < x < 30 km

  • The effects of the clouds are partially prognosed from
  • predicted fields: water vapor, cloud water and ice, and frozen
  • and liquid precipitation.
  • Some portions of the cloud effects are still diagnosed (parameterized).
  • some precipitation
  • some vertical transport and redistribution of moisture and heat
  • turbulence
representation of clouds in atmospheric models6
Representation of Clouds in Atmospheric Models

Cloud-scale models: 100 m < x < 8 km

The effects of the clouds are entirely prognosed from

predicted fields: water vapor, cloud water and ice, and frozen

and liquid precipitation.

problems with modeled clouds
Problems with Modeled Clouds

Large-scale models (clouds completely diagnosed):

Poor diagnosis of cloud type, composition, and precipitation.

Clouds and cloud-systems do not know about vertical wind shear.

Implications:

(1) Large uncertainty in climate-model predictions

(2) A key limiting factor for weather-forecast accuracy

slide8

Meso-/Cloud-Scale Model (WRF)

Hurricane Katrina Reflectivity at Landfall

29 Aug 2005 14 Z

4 km WRF, 62 h forecast

Mobile AL Radar

slide9

Realtime WRF 4 km BAMEX Forecast

12 h forecast Initialized 5/24/03 00Z

Reflectivity Forecast

Composite NEXRAD Radar

slide10

Realtime WRF 4 km BAMEX Forecast

12 h forecast Initialized 5/24/03 00Z

Reflectivity Forecast

Composite NEXRAD Radar

vertical velocity at z 5 km t 5 h

Vertical Velocity at z = 5 km, t = 5 h

Along-line cell spacing ~ 6 to 8 Dh until Dh < 500 m

(cell diameter is 3 to 4 km in converged solutions)

(Courtesy of G. Bryan, NCAR/MMM)

simulations using x 4 km to x 250 m

x = 4000 m

Simulations using x = 4 km to x = 250 m

x = 1000 m

Weak-shear case:

Vertical cross-section

of tracer concentration

at 6 h (not a line-average).

x = 250 m

(Courtesy of G. Bryan, NCAR/MMM)

surface rain rate weak shear

Surface rain rate, weak shear

250 m solution close to convergence

1, 2, 4 km solutions over-predict precipitation.

(Courtesy of G. Bryan, NCAR/MMM)

problems with cloud models

Problems with Cloud Models

When will our applications get there?

(assume comp. speed doubles every 18 months)

Climate- not in my lifetime

Weather - global (state-of-the-art h ~ 25 km)

36 years (maybe in my lifetime)

Weather - regional (state-of-the-art h ~ 7 km)

19 years

(hopefully in my lifetime – but will I be retired?)

Solutions do not statistically converge until

h < O(100 m) - turbulence problem

cloud models

Cloud Models

Cloud models solve the 3D Euler equations and transport equations for water vapor and liquid/solid water species with subgrid models for turbulence and other models (parameterizations) for everything else (moisture phase changes, radiation, land-surface, ocean-surface, etc.)

Generally speaking, there are 2 flavors:

(1) Semi-Implicit (implicit treatment of acoustic and gravity waves)

usually found in global models on lat-long grids – pole problem.

(2) Explicit (explicit treatment of acoustic and gravity waves)

some form of splitting is usually used to advance acoustic and

gravity waves with a shorter timestep.

slide16

WRF-ARW

  • Terrain-following hydrostatic pressure vertical coordinate
  • Arakawa C-grid
  • 3rd order Runge-Kutta split-explicit time integration
  • Conserves mass, momentum, entropy, and scalars using flux form prognostic equations
  • 5th order upwind or 6th order centered differencing for advection
  • Limited area (not global)

(more info - http://www.mmm.ucar.edu/wrf/users/)

why explicit
Why Explicit
  • Explicit time integration with splitting is more efficient than implicit solvers (operations for a given level of accuracy).
  • Solver needs little tuning for application at different grid resolutions and problem sizes.
  • Easily parallelized for SM, DM and SM/DM architectures.
slide18

Time Integration in ARW

3rd Order Runge-Kutta time integration

advance

Amplification factor

slide19

Time-Split Runge-Kutta Integration Scheme

dt is the RK3

timestep

acoustic timestep

(in this case dt/4)

slide20

Time-Split Runge-Kutta Integration Scheme

In DM applications:

A small amount of data is communicated within each acoustic step.

slide21

Time-Split Runge-Kutta Integration Scheme

In DM applications:

A small amount of data is communicated within each acoustic step.

A larger amount is data is communicated after each RK substep.

parallelism in wrf multi level decomposition
Single version of code for efficient execution on:

Distributed-memory

Shared-memory

Clusters of SMPs

Vector and microprocessors

Parallelism in WRF: Multi-level Decomposition

Logical domain

1 Patch, divided into multiple tiles

Inter-processor communication

Model domains are decomposed for parallelism on two-levels

  • Patch: section of model domain allocated to a distributed memory node
  • Tile: section of a patch allocated to a shared-memory processor within a node; this is also the scope of a model layer subroutine.
  • Distributed memory parallelism is over patches; shared memory parallelism is over tiles within patches
wrf software framework overview
Implementation of WRF Architecture

Hierarchical organization

Multiple dynamical cores

Plug compatible physics

Abstract interfaces (APIs) to external packages

Performance-portable

Top-level Control,

Memory Management, Nesting,

Parallelism, External APIs

driver

ARW solver

Other Solvers

Physics Interfaces

mediation

Plug-compatible physics

Plug-compatible physics

Plug-compatible physics

Plug-compatible physics

model

Plug-compatible physics

WRF Software Framework Overview
slide24

Courtesy of J. Michalakes; see http://box.mmm.ucar.edu/wrf/WG2/bench/ for more info

petascale computing and clouds

Petascale Computing and Clouds

Many effects of clouds on climate and weather are largely unknown/uncertain (observations lacking, models at coarse resolution have poor representation of clouds). Most important problem confronting dynamicists and modelers today.

Cloud-resolving (Dh ~ O(100 m)) simulations of cloud systems are needed to understand cloud dynamics and to improve parameterizations - a petascale computing challenge.

cloud-

mixing

eddies

cloud

systems

planetary waves

synoptic systems

clouds

>106 meters

105 - 106

meters

102 - 104

meters

meters to

100’s meters

petascale computing and clouds26

Petascale Computing and Clouds

Split-explicit cloud models are easiest to scale to peta-computing - no global data exchange or implicit solver needed, numerics are not scale dependent.

We can scale our problems to bigger machines.

Questions:

What will new machine architectures look like?

Will we maintain efficiency with scaling and changes in machine architecture?

What code architecture changes will be needed?

Other problems: load balancing, analysis, I/O.