Evaluation of the Latent heat nudging scheme for the rainfall assimilation at the meso-gamma scale

Andrea Rossa* and Daniel Leuenberger

MeteoSwiss

*current affiliation: ARPA Veneto , Centro Meteo Teolo

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Motivation

QPF poorest performance area of NWP, especially in summer (convection)

Rainfall assimilation to mitigate spinup effect

Radar observations for high-resolution NWP models

Characteristics of LHN

Buoyancy driven

Computational efficiency

4DDA, timely assimilation of high-frequency observations

This study: reevaluation of Latent Heat Nudging (LHN) within high-res model using idealised and real radar data

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Idealised Experiments: Setup and Strategy

Model: Local Model (LM) in idealised configuration

Unstable environment supportive for supercell storms

Reference simulation

Surface precipitation for assimilation

4D fields for validation

Assimilation simulations

Proof of concept with identical twin simulation (CTRL)

Sensitivity to uncertainty in observations and environment

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Reference and CTRL simulation

REF

total sfc rain RH, W and e, W and cold pool

x x x

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CTRL

+ 5 %

x x x

y yz

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Extrema of w

Delay: 12 min

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Assimilation increments

Transient phase: 45 min

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Observation Uncertainty

Amplitude Error

Factor 0.5 (2) results in 88% (133%) of total precipitation

Environment and model dynamics damps error

Temporal resolution

Combined amplitude and structure error

Strong sensitivity

Non-Rain Echoes

Potential strong sensitivity

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Non-Rain Echoes: convective conditions

6h Sum of Radar 6h Sum of Model Precipitation

x x

y y

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Non-Rain Echoes: AP conditions

Vertical mixing due to strong, spurious updrafts

x

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w

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Environment Uncertainty

Perfect observations, degraded environment

Low-level Humidity

-4% (4%) error in PBL humidity -14% (11%) precip. deviation

For slightly drier conditions: convection suppressed

For slightly moister conditions: multi-cell development

Environmental Wind

Can lead to distorted system dynamics

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Uncertainty in the Environment: Error in Wind

Surface precipitation TLHN, cloud, precip , W and cold pool

Bias in environmental wind: +2m/s; assimilation only

x x x

yzy

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Case study of 8. May 2003 21 UTC

200 km

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Simulations with x = 2.2km, no CPS

4h Forecast21-22 UTC

CTRL RADARLHN

Analysis17-18 UTC

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8. May 2003 Storm

LHN Analysis RADAR1h Forecast RADAR

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Findings

LHN has good potential for triggering convection

Excellent results in identical twin simulation

Scheme sensitive to observation and environment errors

Non-rain echoes pose serious problem Data quality

Case study of real stormLHN triggers storm at right location and intensityRadar data clearly improves QPF in the first hoursOutflow located too far downstream position error Storm kept in model for more than 4h

Storm environment important, particularly PBL! Use mesoscale data assimilation to complement rainfall assimilation (AWS, VAD/radial winds, GPS tomography, PBL profilers)

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6Thank you for your attention !