stefan klink, klaus stephan and christoph schraff [email protected]

05.08.2005 - 1 -COSMO General Meeting

COSMO General MeetingZürich, 20 - 23 Sept 2005 [email protected]

Stefan Klink, Klaus Stephan and Christoph [email protected]

[email protected]

[email protected]

Recent developments in Latent Heat Nudging at DWD

• “blacklist” for radar data

• latent heat nudging and “prognostic” precipitation

• results of assimilation experiments

• summary and aspects of future work



“Blacklist” for radar data

• Motivation: detection of ground clutter, which is not removed by the operational Doppler filter,clutter occurs at many of these grid points more or less permanently

• approach: consistency of radar derived precipitation rate and satellite derived cloud-type product(Met-8 Nowcasting-SAF) at each grid point over a longer time period (e.g. 15 days)

• blacklist criterion: at 20% of the cloud-free dates, a non-zero precipitation rate has to be found at this special grid point

• Usage during assimilation: blacklisted grid points are not treated by the LHN-algorithm

• Plan: update this mask once a month, taking at least the last 30 days into consideration

total sum of precipitation derived from the DX radar composite



Hourly accumulated precipitation height




test case with “prognostic” precipitation



“Prognostic” Precipitation

diagnostic prognostic

• just a diagnostic expression for the precipitation flux :

• full prognostic equation for the mass fraction :

xP

xx Sz

P

1

xq

xx

xx S

z

Pqv

t

q

1

• precipitation falls immediately and in

the same column, in which it has been formed.

t = 0 ; xy = 0

• precipitation falls with a certain sedimentation velocity through the model space and could be advected

horizontally , too.

t > 0 ; xy 0



Latent heat nudging and “prognostic” precipitation

LHN-Assumption: vertically integrated latent heat release precipitation rate

55km

yx

The correlation between the vertically integrated rate of latent heat release and the surface precipitation rate is significantly smaller in the simulation with prognostic precipitation



diagnostic prognostic

• Integration within one column • Integration along a path

dzLHyxrrtyxz

tzyxqc

0),,(

0),,,(

00

000

0000

),( dlLHyxrrtyxz

tzyxqc

0),,(

0),,,( 0000

),(

dlheatlatentofreleaserateionprecipitatground

formationcloud

• The precipitation rate reflects the integral of latent heat release along the path of precipitation particles through the atmosphere from the formation of cloud droplets until the contact with the ground.



+

-from: R. A. Houze, Jr.: Cloud DynamicsInternational Geophysics Series Vol. 53

• main part of positive latent heat release occurs in updrafts, strong precipitation rates are often related to downdrafts

• at x < 3 km , with prognostic treatment of precipitation (model resolves large clouds): model is able to distinguish between updrafts and downdrafts inside convective systems

horizontal displacement of areas with strong latent heating resp. to surface precipitation,

modified spatial structure of latent heat release in the model

scheme will notice only with temporal delay if precipitation already activated by LHN



possible adaptations I

• horizontal displacement of vertically integrated latent heating and surface precip.:

strong horizontal smoothing applied to 2D fields of observed and modelled surface precipitation rate and to 3D field of latent heat release,

in order to get higher correlation of precipitation and latent heating

(is found to have limited impact, tends to decrease precipitation amounts)



possible adaptations II

• change of the spatial structure of latent heat release in the model:

– updraft regions (at the leading edge of a convective cell): very high values of latent heat release, little precipitation

higher values of the scaling factor and of LHN increments often occur

mo

obsLHmoLHN RR

RRTT , 1

reduce upper limit of the scaling factor

adapt grid point search routine

– downdraft regions (further upstream): high precipitation rate, weak latent heat release (often negative in most vertical layers)

LHN increments are inserted only in the vertical layers where the model latent heating rates are positive (approx. in cloudy layers)

(to avoid e.g. negative LHN increments and cooling where the precipitation rate should be

increased)



possible adaptations III:

• temporal delay effect (generated precipitation reaches the ground with some delay):

an immediate reference information, on how much precipitation the temperature increment has initialised already, is required within each time step

use of a ’reference precipitation’ RRref: refRR

RR

RR

RR obs

mo

obs

• diagnostically calculated precipitation rate (by additional call of diagnostic precipitation scheme without any feedback on other model variables)

• vertically averaged precipitation flux (more consistent, however it does not eliminate the temporal delay completely)

for LHN: temporal delay effect found to be much more important than spatial displacement



Experiment for 07 – 18 July 2004 (mainly convective precipitation events)

Model setup:

general settings• LM version 3.13• LMK configurations (2-TL-scheme, SL advection scheme)• continuous assimilation cycle, 00-, 12-, 18- UTC forecasts

LHN experiment• ‘undelayed’ reference precipitation (vertically averaged precipitation flux)• applying temperature increments ‘only in clouds’• upper limit of scaling factor = 2, lower limit = 0.5• analysed 3D precipitation fields not passed to the forecasts• laterally nested into GME

CTRL experiment• laterally nested into LM



12 UTC forecasts, ETS precipitation (hourly heights), threshold values: 0.1, 0.5, 1.0, 5.0 mm0.1

5.01.0

0.5



Assimilation, FBI precipitation (hourly heights), threshold values: 0.1, 0.5, 1.0, 5.0 mm0.1

5.01.0

0.5



Experiment for 07 – 18 July 2004 (mainly convective precipitation events)

Model setup:

general settings• LM version 3.15• new LMK configurations (2-TL-scheme, Bott advection scheme)• continuous assimilation cycle, 00-, 12-, 18- UTC forecasts

LHN experiment• ‘undelayed’ reference precipitation (vertically averaged precipitation flux)• applying temperature increments ‘only in clouds’• upper limit of scaling factor = 1.7, lower limit = 0.3, + logarithmic scaling• analysed 3D precipitation fields passed to the forecasts• laterally nested into LM

CTRL experiment• laterally nested into LM

result of a 4-day intermediate experiment:FBI for 5.0 mm threshold during assimilation (LM 3.13, nested into GME):

revised version decreases overestimation of precipitation compared to previous LHN version



assimilation

2.0 mm

FBI

ETS

0.1 mm threshold values

scores for hourly precipitation : with latent heat nudging / without latent heat nudging



12-UTC forecasts

2.0 mm

FBI

ETS



… and similar results for 18-UTC forecasts



18-UTC forecasts

2.0 mm

FBI

ETS



… and similar results for 18-UTC forecasts



0-UTC forecasts

2.0 mm

FBI

ETS





verification against German radiosondes, 11-day period (8 – 18 July 2004): dashed: with latent heat nudging / solid: without latent heat nudging

bias

temperature

relativehumidity

+ 0 h + 6 h + 12 h + 18 h

more stable

colder

drier

moister



verification against German radiosondes, 11-day period (8 – 18 July 2004): dashed: with latent heat nudging / solid: without latent heat nudging

r m s e

t e m p e r a t u r e

r e l a t I v e h u m I d I t y

+ 0 h + 6 h + 12 h + 18 h

worse

better



• ‘blacklist’ for radar data: avoids introduction of spurious rain at radar locations

• several adaptations to LHN to cope with prognostic precipitation; most important:use of an ‘undelayed’ reference precipitation (vertically averaged precipitation flux)

• revised LHN, assimilation mode: – simulated rain patterns in good agreement with radar observations, – overestimation of precipitation strongly reduced – strong gravity waves induced LHN forcing too strong (?)

• subsequent forecasts, impact on precipitation (10-day summer period):– large positive impact for 4 hours (longer than in simulations with diagnostic precip)– mixed ETS impact beyond + 6 h (interpretation yet unclear,

need verification without ‘double penalty’)– frequency bias slightly improved, increased values from + 0 h to + 6 h,

slightly decreased values from + 6 h to +

12 h

• upper-air verification (11-day summer period): – LHN cools and dries PBL, increases mid-tropospheric stability and upper-

tropospheric moisture– overall neutral impact on rmse of forecasts, temperature improved in PBL at + 6 h

moderately degraded around 700 hPa later on

Summary of Results



• more case studies for both summer and winter periods (up to 10 days),

(further problems ?)

• further diagnosis of LHN results,

in order to better understand some problems (e.g. performance of 0-UTC runs, decrease of forecast impact, too strong LHN forcing) and improve / tune LHN scheme

– role of gravity waves– vertical structure of precipitating cells (e.g. wind field)– vertical distribution of LHN increments– environment of precipitation cells (moisture convergence)– horizontal filtering

• introduction of PI-data (international composite) outside the German DX-area

• further use of cloud type product of Nowcasting-SAF within LHN (humidity adjustment)

• LMK test suites (periods up to three months), with comprehensive verification

• ( possibly: use of 3D reflectivity data in order to determine more precisely (both in space and time) the areas of positive latent heat release

height-dependent scaling of the latent heat rate )

Aspects of Further Work



Prognostic Precipitation and LHN - Influence on LHN

spec. cloud water

spec. cloud ice

latent heat rate

spec. rain content

spec. snow content



• Possible adaptations:

– Adjusting the controlling LHN parameters

– Applying of the temperature increment only in certain layers

– Adaptation of the grid point search routine

– Spatial Averaging of precipitaion patterns and the pattern of latent heat release

– Spatial displacement of radar image

– Use of an undelayed precipitation rate as reference for the LHN

- diagnostically calculated precipitation rate

- vertical integration of the precipitation fluxes

stefan klink, klaus stephan and christoph schraff [email protected]

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