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GSI EXPERIMENTS FOR RUA REFLECTIVITY/CLOUD AND CONVENTIONAL OBSERVATIONS

Ming Hu, Stan Benjamin, Steve Weygandt, David Dowell, Terra Ladwig, and Curtis Alexander

NOAA/ESRL/GSD/EMB

RUA workshop, Boulder06/03/2015

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GSI overview• The Global Statistical Interpolation (GSI) was developed mainly as NCEP operational data analysis system for improving model forecast:• GFS, NAM, RAP/HRRR, HWRF, …

• GSI can analyze many kinds of observations:• Conventional, radiance, radar, GPS, …

• GSI analysis cores:• 3DVAR, ensemble-var hybrid

• GSI background can be:• GFS, NMMB, NNM, ARW, …

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Analysis versus initial condition

• GSI aims to generate better initial condition which make the forecast better:• GSI analysis results may not fit to the observations

closely.

• Analysis requires the analysis results fit to the observation closely to reflect the “true” atmosphere status.

• RTMA is the only function in GSI to do 2D analysis

• But GSI can be configured to conduct 3D analysis

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GSI is analysis system• GSI modifies the background to fit to the observations

based on the ratio of observation error and background error• For forecast

• Balance the weight between background and observation

• For analysis:• Weight the observation more (small observation error)• Reduce the impact radius• Less balance

• Benefit of using GSI:• Work with WRF-arw, nmm, nmmb, and GFS• Advanced analysis method: 3DVAR, hybrid• Observation operators available for many kinds of obs• Observations are QCed inside and outside GSI• Community code

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GSI for forecast: RAP exampleBackground (01) and analysis (03) fit to observations

Wind o-g 01 all count 3531 8625 4930 6847 5904 3686 2830 9472 5161 1074 404 53428 o-g 01 all bias -0.18 -0.01 0.30 0.50 0.29 0.21 0.05 0.12 0.06 -0.05 1.27 0.18 o-g 01 all rms 2.38 2.79 3.06 3.09 3.05 3.52 3.75 3.83 4.06 4.85 4.96 3.42

o-g 03 all count 3559 8614 4925 6849 5902 3683 2830 9478 5179 1078 404 53465 o-g 03 all bias 0.08 0.24 0.44 0.53 0.31 0.18 0.12 0.15 0.09 0.30 0.93 0.26 o-g 03 all rms 2.09 2.27 2.52 2.59 2.38 2.69 2.91 3.09 3.33 3.70 3.97 2.75

Temperature: o-g 01 all count 8752 14536 4060 5198 6047 3023 2162 7965 3939 934 396 57701 o-g 01 all bias -0.23 -0.10 0.09 -0.25 -0.13 -0.10 -0.07 -0.21 -0.15 -0.55 -0.22 -0.15 o-g 01 all rms 1.55 1.66 1.88 1.22 0.75 0.70 0.81 0.96 1.10 1.50 1.65 1.36

o-g 03 all count 8764 14526 4059 5197 6047 3023 2162 7965 3939 934 396 57701 o-g 03 all bias -0.03 0.08 0.17 -0.09 -0.03 -0.04 -0.02 -0.04 -0.02 -0.35 -0.20 0.00 o-g 03 all rms 1.42 1.51 1.75 1.07 0.55 0.49 0.58 0.65 0.84 1.23 1.47 1.20

Moisture: o-g 01 all count 4649 7639 1434 1110 654 949 760 935 1177 1089 0 20396 o-g 01 all bias -0.40 1.49 -0.34 -1.18 -3.02 -1.62 -2.10 -2.07 -4.76 -5.52 0.00 -0.54 o-g 01 all rms 6.99 6.81 6.95 9.43 11.45 14.27 15.89 20.37 21.95 19.62 0.00 11.28

o-g 03 all count 4649 7636 1434 1110 654 949 760 935 1177 1089 0 20393 o-g 03 all bias -0.18 0.23 0.27 0.81 -0.46 0.15 -1.15 -0.46 -1.83 -3.21 0.00 -0.24 o-g 03 all rms 6.05 5.61 5.47 7.11 8.12 10.72 11.11 14.25 15.50 15.17 0.00 8.52

Wind RMS reduced 19.6% after analysis; Temperature reduced 11.8%; Moisture reduced 24.5%. Different observation type has different fit rate, such as soudning T reduced 21.1%.

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U Increment from single obs test —Different horizontal impact scale

zoom in

Change ‘hzscl_op’ not only change the horizontal influence scale, but also the weight (how much analysis results fit to the observations)!

0.6~0.7

0.9~1.0

Figures from courtesy of Min Sun

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U Increment from single obs test ——— Different vertical impact scales

0.6~0.7X-Z Plane

Figures from courtesy of Min Sun

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GSI for Analysis with hzscl_op=1/8 and VS=1/2

Background (01) and analysis (03) fit to observations

Wind o-g 01 all count 3531 8625 4930 6847 5904 3686 2830 9472 5161 1074 404 53428 o-g 01 all bias -0.18 -0.01 0.30 0.50 0.29 0.21 0.05 0.12 0.06 -0.05 1.27 0.18 o-g 01 all rms 2.38 2.79 3.06 3.09 3.05 3.52 3.75 3.83 4.06 4.85 4.96 3.42

o-g 03 all count 3561 8612 4925 6850 5902 3690 2835 9479 5172 1079 404 53473 o-g 03 all bias 0.05 0.11 0.17 0.27 0.16 0.05 0.05 0.04 0.06 0.10 0.43 0.12 o-g 03 all rms 1.19 1.39 1.58 1.56 1.31 1.00 0.95 0.93 1.04 1.53 2.50 1.33

Temperature: o-g 01 all count 8752 14536 4060 5198 6047 3023 2162 7965 3939 934 396 57701 o-g 01 all bias -0.23 -0.10 0.09 -0.25 -0.13 -0.10 -0.07 -0.21 -0.15 -0.55 -0.22 -0.15 o-g 01 all rms 1.55 1.66 1.88 1.22 0.75 0.70 0.81 0.96 1.10 1.50 1.65 1.36

o-g 03 all count 8761 14529 4057 5199 6047 3023 2162 7965 3939 934 396 57701 o-g 03 all bias -0.12 0.00 0.17 -0.12 -0.04 -0.05 -0.03 -0.05 -0.05 -0.45 -0.18 -0.04 o-g 03 all rms 1.28 1.40 1.66 1.02 0.46 0.44 0.52 0.56 0.74 1.29 1.47 1.11

Moisture: o-g 01 all count 4649 7639 1434 1110 654 949 760 935 1177 1089 0 20396 o-g 01 all bias -0.40 1.49 -0.34 -1.18 -3.02 -1.62 -2.10 -2.07 -4.76 -5.52 0.00 -0.54 o-g 01 all rms 6.99 6.81 6.95 9.43 11.45 14.27 15.89 20.37 21.95 19.62 0.00 11.28

o-g 03 all count 4649 7639 1434 1110 654 949 760 935 1177 1089 0 20396 o-g 03 all bias -0.09 0.45 0.30 0.16 -1.11 -0.28 -1.24 -0.62 -2.43 -4.00 0.00 -0.30 o-g 03 all rms 4.82 4.64 4.76 6.46 7.41 9.64 10.05 13.10 15.22 15.28 0.00 7.80

Wind RMS reduced 61.1% after analysis; Temperature reduced 18.4%; Moisture reduced 30.8%. Much less than the single observation test because redundant data and balance in BE.

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GSI for Analysis with hzscl_op=1/8 and VS=1/2 + ¼ of observation error

Background (01) and analysis (03) fit to observations

Wind o-g 01 all count 3428 8525 4848 6833 5898 3373 2643 9137 4747 958 404 51757 o-g 01 all bias -0.22 -0.02 0.29 0.49 0.29 0.34 0.12 0.15 0.10 -0.03 1.27 0.19 o-g 01 all rms 2.13 2.72 2.98 3.07 3.03 3.24 3.55 3.63 3.75 4.42 4.96 3.25

o-g 03 all count 3493 8555 4876 6841 5900 3430 2692 9254 4878 982 404 52269 o-g 03 all bias 0.03 0.08 0.12 0.20 0.11 0.04 0.02 0.02 0.03 0.05 0.35 0.08 o-g 03 all rms 1.06 1.25 1.43 1.37 1.10 0.82 0.72 0.73 0.84 1.46 2.43 1.17

Temperature: o-g 01 all count 8751 14492 4056 5198 6046 3023 2162 7965 3939 934 396 57651 o-g 01 all bias -0.23 -0.12 0.08 -0.25 -0.14 -0.10 -0.07 -0.21 -0.15 -0.55 -0.22 -0.15 o-g 01 all rms 1.55 1.60 1.86 1.22 0.75 0.70 0.81 0.96 1.10 1.50 1.65 1.34

o-g 03 all count 8762 14519 4058 5196 6047 3023 2162 7965 3939 934 396 57690 o-g 03 all bias -0.06 0.03 0.16 -0.07 -0.01 -0.01 -0.01 -0.01 -0.01 -0.15 -0.08 0.00 o-g 03 all rms 1.20 1.27 1.54 0.95 0.31 0.26 0.28 0.30 0.43 0.83 1.12 0.97

Moisture: o-g 01 all count 4641 7623 1433 1107 643 931 742 894 1107 1063 0 20184 o-g 01 all bias -0.34 1.52 -0.31 -1.14 -2.73 -1.37 -1.69 -2.30 -4.20 -5.51 0.00 -0.42 o-g 01 all rms 6.82 6.60 6.87 9.22 9.95 12.39 13.91 16.56 17.17 17.84 0.00 9.88

o-g 03 all count 4643 7628 1434 1109 652 943 753 919 1146 1072 0 20299 o-g 03 all bias 0.01 0.36 0.35 0.11 -0.73 0.28 -0.75 -0.58 -1.54 -3.43 0.00 -0.16 o-g 03 all rms 5.13 4.56 4.64 6.17 6.56 8.19 7.96 10.12 11.23 13.41 0.00 6.75

Wind RMS reduced 64% after analysis; Temperature reduced 27.6%; Moisture reduced 31.7%.

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GSI for forecast: RAP surface analysis increment

Wind RMS reduced 19.6% after analysis; Temperature reduced 11.8%; Moisture reduced 24.5%.

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GSI for Analysis with hzscl_op=1/8 and VS=1/2

Surface analysis increment

Wind RMS reduced 61.1% after analysis; Temperature reduced 18.4%; Moisture reduced 30.8%.

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GSI for Analysis with hzscl_op=1/8 and VS=1/2 + ¼ of observation error

Wind RMS reduced 64% after analysis; Temperature reduced 27.6%; Moisture reduced 31.7%.

Surface analysis increment

13GSI Cloud Analysis: RAP at 18Z 06/02/2015

background

analysis

Cloud top Cloud base

Cloud baseCloud top

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Analysis

PODy 1000 feet ceiling PODy 3000 feet ceilingPODy 500 feet ceiling

With cloud analysis

Without cloud analysis

6h fcst

RAP Cloud Ceiling Verification: PODY

To keep cloud in forecast: adjust moisture and temperature in cloudy area and clear area

15GSI reflectivity analysis

background

RAP analysis2D reflectivity

RUA analysis 3D reflectivity

00Z 08/10/2014Composite ref

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GSI reflectivity analysiscross section at j=224 and I=385:435

X

XX

Z

ZZ

background

RAP analysis ref

RUA analysis ref

QSNOW at column (i=411,j=224)

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Summary

• GSI is ready to be used for the analysis• GSI analysis can include conventional observation, cloud, radar and other observations

• Background error covariance and observation errors should tuned for the analysis

• GSI has many advantages, most important one is function to use ensemble to add flow depend information into the analysis (David’s talk)