Radar Data Assimilation

Radar Data AssimilationFor Severe Convective Weather

By Kyle Ziolkowski

Outline1. Introduction

What is radar data assimilation?Why is it important?

2. How is radar data assimilated? Different data assimilation techniques.Transformation/relationships of radial velocity and

reflectivity products to model variables3. Improved quality of QPF

Case examples

Radar Data Assimilation

Introduction

1. Introduction One of the major issues of operational forecasting is the

location and timing of precipitation also known as Quantitative Precipitation Forecasting (QPF).

Additionally, it is difficult to forecast the timing and location of deep moist convection.

Some higher resolution models (NAM, RAP, and the HRRR for example) have simulated reflectivity or composite reflectivity which attempt to forecast the timing and location of deep moist convection.

In order for these types of high resolution models to preform, they require lots of reliable data from the boundary layer in order to improve QPF, particularly QPF from convection.

1. Introduction Radar data assimilation has long been viewed as a method to help

improve QPF Radars observe with high temporal resolution obtaining lots of

information on the current state of the atmosphere, and are able to locate and track precipitation.

High spatial resolutions of radars on the order of a few kilometres match that of high resolution models.

Radiosondes and satellite information are sufficient for observing the synoptic setting. However they do not provide enough information in terms of convective scale processes.

With the advancements in computing power, and higher spatial coverage with radars, it is now possible to assimilate radar data to help improve QPF.

Radar Data AssimilationHow is Radar Data Assimilated

2. How Is Radar Data Assimilated

Lots of Data! Roughly 3 million data points are

observed every 5 minutes (Wang, 2013).

This number decreases after quality control procedures.

Ideal for Convective scale models

Radar data provided to the models is reflectivity and radial velocity data, and unfortunately these are not model variables.

Therefore relationships are derived for both in order for the model to ingest the data.


Radar data assimilation techniques. Successive Correction Newtonian Nudging 3D-Var 4D-Var Ensemble Kalman Filter

(EnKF)

Each have different methods/advantages/disadvantages when considering radar data assimilation


3D-Var Most widely used Advantages:

Can directly assimilate variables that are not model variablesUses a recursive filter which incorporates mass continuity into

the cost function. Useful for radial wind components. Information on total hydrometeors, and total water vapor helps

balance other moisture variables to the microphysics schemes used.

Can include vertical velocity via the Richardson’s Eq. Disadvantage – Struggles with crossbeam component winds

and convective scale thermodynamic disturbances for background error statistics, which were originally designed for larger scale observations.


4D-Var/EnKFCan perform the same functions as 3D-Var but also

incorporates the time component in which is useful for rapidly changing phenomena such as precipitation.

Can directly assimilate rainfall data from radars into the model without deriving it from divergence, moisture, and heating.

Disadvantage – Computationally expensive, but is becoming more widely used with the advancement of computing power.


Transformation of Radar data to Model Variables First, the radar data needs to be converted from polar to Cartesian

coordinates. Reflectivity data

A relationship is created through the rain-water mixing ratio q r

Note: This equation assumes a Marshall-Palmer DSD, so it can be subject to errors based on this assumption

qr can be used to to find total cloud water/water vapor, and rain water. Note: typically a warm rain microphysics scheme is applied to bridge

hydrometeors to other variables Issues: Model may not show rain where rain exists if the first guess

field shows no RH in that region.


Transformation of Radar data to Model VariablesRadial Velocity

Where:(x,y,z) = model location(xrad, yrad, zrad) = radar observation locationr = distance between the model grid point and the radar

location(u, v, w) = model velocity variablesVT = terminal velocity of rain


Transformation of Radar data to Model VariablesRelationship of VT to qr


Transformation of Radar data to Model VariablesWe are interested in the information we can obtain

for u, v and w and we can see their relationship from the radial velocity equation (mainly interested in the w component!).

Furthermore we can obtain vertical velocity from the Richardson Eq.


Transformation of Radar data to Model Variables Rishardson Eq.

Richardson’s equation is a higher-order approximation of the continuity equation than the incompressible continuity equation or anelastic continuity equation.

It can build an efficient linkage between dynamic and thermodynamic fields because the thermodynamic equation is directly involved.


Notes:Radar data assimilation is only as good as the

radar processing!Quality control is a major step when concerning

radar data assimilationVelocity dealiasingSecond trip echoesGround clutterAnomalous Propagation

Radar Data Assimilation

Improved Quality of QPF

Improvements to QPF The assimilation of radar data

has shown to improve the timing and the location of precipitation.

The figure by Xiao et al. shows the Threat and Bias scores averaged for a 24hr forecast over two seasons in Korea.

2005, no radar data was being assimilated into the WRF

2006, radar data was being assimilated (note the difference in bias and threat scores).

(Xiao et al, 2008)

Improvements to QPFHow does this match up against other models

with/without radar data assimilation?

(Sun, 2010)

Improvements to QPFWe can see that radar data assimilation isn’t

failsafe, but does improve the location of precipitation along with the shape.

How does radar data assimilation improve mesoscale forecasts?

Improvements to QPF The following is an experiment

set up by Xue et al. (2014) and they show how radar data assimilation helps with the location and timing of convection. They also show that with increasing resolution, the detail in the storm structure becomes more realistic and that radial velocity is important to this fact.

The figure on the right is at a grid spacing of 1km and only assimilates data from the nearby KTLX radar.

Improvements to QPF Here the data is stopped

being fed into the model and the model is now performing a “free-forecast”

Note how the structure is maintained and how it compares to the actual observations

Improvements to QPF Note how the radial

velocity field shows the strong mesocyclone, but it is displaced slightly northward. This could be due to

other dynamics from the parent model

SummaryRadar data assimilation doesn’t fully resolve the

timing and location of precipitation but rather it helps improve the quality of the analysis field.

We can see from the examples that it does preform well but can fail

The analysis is only as good as the processing of the radar data!

Very complex process; lots of data being fed into the model at grid spacing's which are close to that of the model grid spacing’s.

Thank You!

References Lord, S., DiMego, G., & Parrish, D. (2006). Progress on Radar Data Assimilation

at the NCEP Environmental Modeling Centre. National Weather Service, National Centres for Environemtal Prediction. College Park, MD: NECP.

Smith, T., Gao, J., Calhoun, r., Stensrud, D., Manross, K., Ortega, K., et al. (2014). Examination of a Real-time 3D-Var Analysis System in the Hazardous Weather Testbed. Weather and Forecasting , 29 (1), 63-77.

Sun, J. (2005). Convective-scale assimilation of radar data: Progress and challenges . Quarterly Journal of th Royal Meteorological Society (131), 3439-3463.

Wang, H. (2013, January 10). Recent Development of WRF 3/4D-Var Radar Data Assimilation. Retrieved March 23, 2014, from American Meteorlogical Society : https://ams.confex.com/ams/93Annual/webprogram/Paper216169.html

Xiao, Q., Lim, E., Won, D.-J., Sun, J., Lee, W.-C., Lee, M.-S., et al. (2008, January). Doppler Radar Data Assimilation in KMA's Operational Forecasting. Bulletin of the American Meteorological Society , 39-43.

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Radar Data Assimilation