DA needs for nowcasting

19 people

  1. specific development/improvement

Luc – observation correlations

Sue studies have shown very short range

Bias – may be quality control issue

What do we know about R, dependent on resolution – need greater than 2-3 times beam width. At 1-2 km scale , beam width – what does cell mean, reflectivity. Strong gradients – how much does it matter?

Beam attenuation issues for reflectivity

C,X band severe problem – OU build into operator

Iszka X.BAND kdp

Observation based correction or use forward operator? 1+3-D beam along beam?

Need to do it quickly so may need short-cuts for obs processing ahead of time

Fall speed not a problem at low beam angles. But are seeing a bias

If observation operator very nonlinear – big problem if bad first guess,

Can we make the problem more linear? MF – seek around area so get more sensitivity/better first guess. Cycling should improve first guess

Cycling with ongoing MCS may be harder – fronts more linear – multiple scale interactions – with diabatic DFI initialization. What are the drivers? Cold pools/outflow – what scales?

RR – cold pools come from precip. May be too hard to get cooling initialized – what obs tell us about that.

Better use of radial velocity – good background term

Thinning, superobbing, observation errors

Radar refractivity – too close to radar –

Need to understand the problem and what will help with the problem

Cold pool very sensitive to microphysics timescale mismatch, CASA network low level wind network very helpful to correct gust front location, 5min mesonet data helps

Clear air Doppler can help with convergence lines – Taiwan threshold hides clear air winds – sea clutter contamination, mountain contamination

MF get more positive impact by removing clear air echoes –but don’t give up

WRF has problem dealing with clear air – VDRAS OK so may be technique

Ground clutter – lower velocity to convergence to need very good quality control – no motivation of no client – need to talk to radar data providers

C-band insects are below the noise level. Need to improve the signal processing

Wrong environment for DA nowcasting – how to modify the forcing? Need to deal with synoptic scale

Need a 4D DA system to get large scale as well as convective/storm scale

Need to improve the low level analysis – CAPE, shear

Need to look at whole 3D-structure and use satellite data

Can 4D-DA modify the environment – needs continuous assimilation

Need big profiler networks – deep winds

Problem with driving model – need larger domain

Problem with phase errors – how to correct, radar data cannot correct phase error.

Need inversions all weather – downward pointing lidar – give size of inversion

Thermodynamic profiles

Modelling of dynamical balances – can we simplify them? Covariances derived from ensembles

Weak constraint eg mass-continuity

Balance idea comes from large scale – minimising time tendancy – now need right time tendancy – may be strong pressure tendancy. mimic the synoptic scale still. 2,5km model run to developed convection balance diabatic forcing and vertical velocity – otherwise no good relationships

Freezing mechanism – need to get correct phase to prevent latent heat release, need model close to atmospheric balance – DFI does some of this

Model errors – need to improve models

3 suggestions for international collaboration

Hymex

Isztar – concern about how quickly loses the impact of the data – model lives in its own world

need good flow dependent background error covariances to get good JB eg ensemble based methods or enkf

Jenny thinks BG less important for radar da assimilation

How long will radar data last – predictability limit – hurricanes lasts longer

Try to reach predictability limit by improving model and initial conditions

BG 100km scale winds impt, 1000km height field

Radar impact to 12hours at 20-80km scale with 3km model

We should worry about process verification not user based verification

International collaboration – common test cases? TOMAX field experiment? Japan – need observation database and cleaned data – problem with forward models and observation processing

Single ob experiments, same environment, need the ensemble as well

Some basic states

Eg squall line/MCS similar situation to understand process eg low level cold pool

Identical twin experiments OSSE of squall line

WGNE – large eddies

Issues

Observation errors and correlations

Radar observation operators – eg beam width

MCS assimilation – cold pools

Thinning/superobbong

Background errors

Wrong environment

Look at full 3d structure

Balance

Model errors

Intercomparison – single ob, OSSE of squall line etc