HWRF Masahiro Sawada Dr Visiting Scientist from Meteorological Research Institute JMA Acknowledgements HWRF group at EMC amp DTC contents Background amp Objectives of this study ID: 796090
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Slide1
Impacts of high-resolution Himawari-8 AMVs on TC forecast in HWRF
Masahiro Sawada (Dr)
Visiting Scientist from
Meteorological Research
Institute, JMA
Acknowledgements
: HWRF group at EMC & DTC
Slide2contents
Background & Objectives of this studyWhat is high-resolution Himawari-8 AMV?Model & Experimental designOutline of target cases
ResultsDetailed analysisSummary2
Slide3TC f
orecast skill at JMA a few years ago…
Although track forecast errors have decreased to less than half of those obtained 30 years…the intensity forecast errors exhibit an
increasing behavior. Annual mean official TC forecast errors in RSMC
Tokyo (Ito 2016)
TrackPmin
Vmax
Slide4Introduction & background
Accurately estimating the tropical cyclone (TC) intensity and structure is essential to improve TC forecast and to diagnose numerical model characteristics. Several previous studies show the assimilation of atmospheric motion vectors (AMVs) derived from satellite images benefits TC forecast using WRF/HWRF (Wu et al. 2014, 2015,
Velden et al. 2017, Zhang et al. 2018).
However, data assimilation has not been applied in the WPA basin yet. The main reason is observations around the TC are sparser in the WPA compared to the ATL and
EPA.
Slide5Introduction & background (continued)
Satellite observations, or satellite retrieved products such as AMVs are available globally and over the ocean without (temporal) interruption!Himawari-8
provides high spatiotemporal resolution AMVs and it has a potential to better capture the TC structure and initial conditions of the surrounding environment, leading to more skillful forecasts.
Can we simply expect the high-resolution AMVs fill the sparse observations over the ocean, and is data assimilation in HWRF applicable for WPA basin?
Slide6Purpose of this study
Assess the capability of data assimilation for the WPA
TCsInvestigate what
impacts the high resolution Himawari-8 AMVs have on the
TC forecastsUnderstand
how impacts ariseExplore
better ways to make more effective use of the Himawari-8 AMVs by changing the assimilation method
- TC
surrounding flow
and
track forecast
are improved
.
- DA
in high-resolution will provide better TC structure, reducing intensity forecast error.
Expectation
Slide7Brief description of Himawari-8
Himawari-8 is next generation geostationary satellite (same as GOES-16).
Himawari-8 became operational at JMA in July 2015.16 channels, 10-min full disk scan, 2.5-min targeted region.AMVs are derived from full disk & targeted region scans. AMVs of full disk is routinely provided hourly.
In this study, AMVs of full disk are used for assimilation experiments because it is provided without interruption and can be used to monitor more than two TCs at the same time.
(From JMA webpage)
Slide8What is high-resolution Himawari-8 AMVs?
Current Himawari-8 AMVs~0.3x0.3 deg, hourly
High-res Himawari-8 AMVs (H8AMV) ~0.2x0.2 deg,
30-min (Experimental product of MSC/JMA)
GSI
GSI-AMV
GSI-AMV2Assimilated Himawari-8 AMV for D02: examples
Slide9Model & experimental design
Name
Description
CTL
cycle experiment without DA (vortex initialization only)GSI
cycle experiment with 3DVAR
using the same dataset as GFS
GSI-AMV
GSI+
H8AMV (add AMVs between -1~+1 h from analysis time)
GSI-AMV2
GSI+
H8AMV (add AMVs between -3~+1 h from analysis time)
Model: NCEP Operational HWRF (2017 Operational configuration)
except for POM ocean coupling (for 2017 HWRF, HYCOM is used for WP)
Horizontal resolution: 18, 6, 2km for D01, D02, and D03 respectively
Vertical layer: 61 up to 10
hPa
Initial condition: GFS analysis for D01 and vortex initialization
126 hour forecast
for
each
cycle
72
cycles per one
experiments
Slide10Outline of
targeted Typhoons
Nepartak
(02W
) Meranty(16W)
Megi(20W)70kt/day!Extreme RI
Track & intensity data from JTWC Besttrack
70kt/day!
Extreme RI
30kt/day
2016
Slide11Results
Track forecastTrack verification –all cases–Track verification –each case–Intensity forecast
Intensity verification –all cases–Intensity verification –each case–Size forecastsize
verification –all cases–11
Slide12Track
verification -all cases-
- GSI-AMV2 provides best track forecast for F84-126, followed by GSI-AMV. => The more H8AMV are, the better track forecast skill becomes.- GSI was the worst for F66-120 in 4 experiments. => Only DA in high resolution grid space cannot improve track forecast.
Track error (km)
Slide13Track
verification -each case-
- Positive impact of H8AMV on track comes from Nepartak case.- Degradation can be seen in the Meranti case, unfortunately.
- Neutral for Megi case.- Case to case variability.- Note that track errors of Meranti & Megi cases are quite smaller than that of Nepartak case.
NepartakMeranti
MegiCTL
GSIGSI-AMVGSI-AMV2
Slide14Intensity verification -all cases-
- All experiments were too weak because extreme RI was not captured.- GSI-AMV2 was the largest Intensity errors in all experiments for shorter lead time.
- This would be caused by larger weak intensity bias.HWRFGSI
AMVAMV2
RMSE
BIAS
Intensity verification -each case-
Nepartak
Meranti
MegiHWRFGSI
GSI-AMVGSI-AMV2
<= GSI-AMV2 has smallest errors!
<= GSI-AMV2 has largest errors…
Case-to-case variability is larger in intensity than in track forecasts
Slide16Size verification (BIAS) -all cases-
- It is hard to see differences between experiments, meaning no significant impacts.- In the R34, 50, & 64, the sizes with GSI-AMV2 tend to be small for shorter lead time compared to CTL.
HWRFGSIAMV
AMV2HWRF
GSIAMVAMV2
R34
R50
R64
RMW
Slide17What processes account for impacts?17
High-resolution AMVs =>
Improve track forecast skill, but degrade intensity forecast skill…why?
Diagnosis of forecasted track error based on optimal steering flows -Nepartak case-Comparison of initial vertical structures of axisymmetric mean composite -Meranti case-
Slide18Track error diagnosis –method–18
Estimate optimal steering radius
& depth according to Galarneau & Davis (2013) to identify cause of track error differences. Cyclonic circulation associated
with TC was removed. Steering flow is calculated from 13 different vertical depth (50−650 hPa with
a fixed base at 850 hPa), and 8 different radii (100−800km).
The radius & depth combination that produces the smallest vector difference between the averaged steering flow and the actual track motion is chosen as the optimal steering flow radius & depth.
Slide19Track error diagnosis –TC structure–
19
Results of optimal steering flow diagnosis for Nepartak case.No systematic difference in optimal steering radius and depth...
=> Reduction of TC track error is not caused by the TC structures through beta-gyre effect.=>How about synoptic flows?
Depth (hPa)Radius (km)
For the most of time, 300~400kmFor the most of time, 400~500hPa(850-450hPa ~ 850-350hPa)
Optimal steering radius
Optimal steering depth
CTL
GSI-AMV2
Radius(km)
317
314
Depth(
hPa
)
482
492
Slide20Track error diagnosis –Synoptic flow–
20Steering flow profile difference between GSI-AMV2 & CTL.Steering
flow differences became remarkable with time from upper to lower troposphere.=> Synoptic flow difference is more likely to reduce the track error biases, not caused by TC structure (size & depth) differences.
Vector difference is largeColor:
Vector Difference in steering flows (m/s)Arrows: Difference in steering vectors calculated within
300-km radius of TC center at each forecast lead time. Height (hPa
)
Slide21Weak intensity bias
HWRF
GSIGSI-AMVGSI-AMV2
Why
was intensity forecast with GSI-AMV2
degraded for shorter lead time?
BIAS of Vmax (
kts
)
Slide22Vertical structure difference in Vt
With DA, initial
vortices became stronger outside RMW.=> larger inertial stability.=> higher resistance of radial displacement.
=> would weaken low-level inflow.Composite of vertical Structure axisymmetric mean
Vt at F00.Normalized radius by RMW at 2-km height is used.
Cycle averaged for
3-20 of case 16W.Color: difference between each experiment minus
CTL.
22
TC center
outer
Slide23Vertical structure difference in low-level inflow
Inclusion
of H8AMV actually weakens low-level inflows.
=> slower intensification because of reduction of inward angular momentum transport.
Composite of vertical Structure axisymmetric mean Vr
at F0-24.
Cycle averaged for 3-20.
Color: difference between each experiment minus HWRF.
23
Slide24Vertical structure difference in axisymmetricity
Axisymmetricity
is defined:
Inclusion
of H8AMV enhances asymmetric structure. =>
slower axisymmetrization.
Composite of vertical Structure axisymmetric mean Vt
at F00.
Cycle a
veraged for
3-20.
Color: difference between each experiment minus
CTL.
24
(Miyamoto &
Takemi
2013)
Slide25Vertical structure difference in RH25
Emanuel & Zhang (2017) showed intensity change
is strongly influenced by inner core moisture, seems to be related to negative bias.
Initial vortices in
GSI-AMV2 became drier within inner core region =>slower intensification(Initial vortices in GSI became drier within RMW & at upper troposphere.)
Composite of vertical Structure axisymmetric mean
RH at F00.Cycle averaged for
3-20
.
Color: difference in RH between each experiment minus
CTL.
Contour:
RH
Slide26Brief summary of factors explaining impacts26
High-resolution AMVs =>
Improve track forecast skill, but degrade intensity forecast
skill.1. Improvement of track forecast skill=> Improvement in steering flow, not in TC structure itself2. Degradation of intensity forecast skill=> Weakening of low-level inflows, decrease in axisymmetricity,
and drying within the inner core region
Slide27How can we make effective use of H8AMV?27
How about changing assimilation method, such as hybrid DA instead of 3DVAR?
=>Background error covariance are created from HWRF, so flow-dependent structure & cross-variable correlation would be represented properly.
Slide28Model & experimental design
Name
Description
CTL
cycle experiment without data assimilationGSI
cycle experiment with 3DVAR
using GSI
HGSI
cycle experiment with hybrid DA which uses background error covariance created from HWRF
GSI_AMV2
3D + high-resolution
AMV derived from
Himawari-8
HGSI_AMV2
Hybrid
DA
+ high-resolution
AMV derived from
Himawari-8
Model: NCEP Operational HWRF (2017 Operational configuration)
except for POM ocean coupling (for 2017 HWRF, HYCOM is used for WP)
Horizontal resolution: 18, 6, 2km for D01, D02, and D03 respectively
Vertical layer: 61 up to 10
hPa
Initial condition: GFS analysis for D01 and vortex initialization
126 hour forecast for each cycle
26 cycles per one experiments from 2016.9.8.12 - 14.18 UTC
28
Slide29Track forecast verification
HGSI_AMV2 provides the best track forecast for F42-66 &
F108-126.
The track error was smaller in HGSI_AMV2 than in GSI_AMV2.29
Slide30Intensity forecast verification
The intensity error was significantly smaller in HGSI_AMV2 than in GSI_AMV2.
By applying hybrid DA, weak bias or slow intensification was greatly reduced!30
HWRF
GSIHGSIGSI-AMV2HGSI-AMV2
Slide31Intensity forecast composite
GSI_AMV2
HGSI_AMV2
By using hybrid DA, slow intensification was alleviated.
Still could
not capture maximum intensity &
RI.
31
Black line: Besttrack
Colored line: each cycle run
Slide32Why was intensity bias mitigated?32
High-resolution AMVs (+ 3DVAR)
=> Improve track forecast skill, but degrade intensity forecast skill.High-resolution AMVs + hybrid DA
=> Improve track forecast skill, WITHOUT degradation of intensity forecast! Why?
Slide33Vertical structure difference in
Vt
In HGSI-AMV2, the increase of tangential wind outside RMW was reduced compared to GSI-AMV2.=>alleviate weakening of low-level inflow.
Composite of vertical Structure axisymmetric mean
Vt at F00.Cycle averaged for
3-20 of case 16W.Color: difference between each experiment minus
CTL.33
Normalized radius
from TC center (km)
Slide34Vertical structure difference in low-level inflow
Weakening of low-level inflow was alleviated in HGSI-AMV2. =>slow intensification bias is improved.
Composite of vertical Structure axisymmetric mean
Vr at F00.
Cycle averaged for 3-20.
Color: difference between each experiment minus CTL.
34
Normalized radius
from TC center (km)
Slide35Vertical structure difference in axisymmetricity
Enhancement of asymmetric component by inclusion
of
H8AMV was reduced using hybrid-DA.=>slow intensification bias is improved.
Composite of vertical Structure axisymmetric mean
Axisymmetricity at F00.Cycle averaged for
3-20.Color: difference between each experiment minus
CTL.
35
Normalized radius
from TC center (km)
Slide36Vertical structure difference in RH36
Drying tendency at the inner core region was alleviated in HGSI-AMV2.
=>reduce slow intensification bias.
Composite of vertical Structure axisymmetric mean RH at F00.
Cycle averaged for 3-20
.Color: difference in RH between each experiment minus CTL.
Contour: RH
Normalized radius
from TC center (km)
Slide37Summary
Assimilation of high-resolution AMV by Himawari-8 will be
beneficial for track forecast but it may increase
weak bias of intensity forecast.
Through the diagnosis of optimal steering flows (depth and radius), reduction of track error is caused by improvement
in the environmental flows, not in the representation of TC structure
.
Composite of axisymmetric mean structures indicates three factors related to the weak intensity bias at shorter lead time:
Weakening of low-level inflows due to high inertial stability outside RMW.
Decrease in axisymmetricity at the upper layer & outside RMW.
Drying within inner core region.
37
Slide38Summary (continued) and future work
By combining AMV with hybrid DA, track forecast could be improved
without the degradation of intensity forecast.
Hybrid-DA reduces the weakening of low-level inflows, enhancement of asymmetric component, and drying within the inner core region. These three factors contribute to improvement of weak intensity bias.
38
Future workMore forecast cycles for robust results
Shorter assimilation cycle (hourly or less)
Capability of targeted region (or rapid-scan) AMVs
Slide39Further experiments
Current operation HWRF uses hybrid-DA with background error covariance
(Be) created from GEFS.=> What degree is different in Be between GEFS & HWRF?Rapid-scan AMVs are really denser than AMVs of full-disk scan. But too much?
=> Are rapid-scan AMVs really useful?How about removing vortex initialization?
How about increasing # of outer loop in GSI?How about using FGAT with hourly first guesses?
Slide40Experimental design
Name
Description
CTL
cycle experiment without DA (vortex initialization only)gHGSI
cycle experiment with hybrid-DA
which uses background error covariance created from
GEFS
using the same dataset as GFS
gHGSI-AMV2
gHGSI+H8AMV
gHGSI-RAMV2
gHGSI+H8AMV
+RSAMV
HGSI
cycle experiment with hybrid-DA
which uses background error covariance created from
HWRF
using the same dataset as GFS
GSI-AMV2
GSI+
H8AMV (add AMVs between -3~+1 h from analysis time)
HGSI-AMV2
GSI-AMV2
with
hybrid-DA
which uses background error covariance created from
HWRF
HGSI-RAMV2
HGSI-AMV2
+ RS
AMV
Slide41Track
verification -all cases-
- HGSI-RAMV2 provides best track forecast for F72-126, followed by GSI-AMV2.
=> The more H8AMV are, the better track forecast skill becomes.- gHGSI was the worst for longer lead time in 4 experiments.
=> Only DA in high resolution grid space cannot improve track forecast.Track error (km)
Slide42Track
verification -each case-
- Positive impact of H8AMV on track comes from Nepartak case.
- Degradation can be seen in the Meranti case, unfortunately.- Neutral for Megi case.- Case to case variability.- Note that track errors of Meranti & Megi cases are quite smaller than that of Nepartak case.
Nepartak
Meranti Megi
CTLgHGSIHGSI
GSI-AMV2
HGSI-AMV2
HGSI-RAMV2
Slide43Intensity verification -all cases-
- All experiments were too
weak because extreme RI was not captured.
- HGSI-RAMV2 was
the smallest Intensity bias in all experiments for almost all lead time.- HGSI-RAMV2 was smaller intensity error than that some experiments. Not bad…- gHGSI-RAMV2 was the largest intensity bias for shorter lead time.
RMSE
BIAS
Slide44Intensity verification -each case-
Nepartak
Meranti
Megi
GSI-AMV2 has smallest errors!GSI-AMV2 has largest errors…
CTLgHGSI
HGSI
GSI-AMV2
HGSI-AMV2
HGSI-RAMV2
Slide45Size verification (BIAS) -all cases-
- It is hard to see differences between experiments, meaning no significant impacts
.
R34
R50
R64
RMW
Slide46Slide47Intensity verification -each case-
Nepartak
Meranti
Megi
HWRFGSIGSI-AMV
GSI-AMV2
GSI-AMV2 has smallest errors!
GSI-AMV2 has largest errors…
Case-to-case variability is larger in intensity than in track forecasts
Slide48Size verification -3cases-
- All RMW Size forecast seems to be positive (large) and biases increases with time. - R34 biases are smaller for early forecast lead time and increases with time.
- Bias of RMW is larger that that of R34, for me it feels strange because RMW is smaller than R34 but…
RMW
R34
Slide49Intensity verification (BIAS) –each cases-
- Largest negative bias can be seen in the
Meranti
case, especially AMV2.
- Negative biases are peak between F54-78 for both Nepartak & Meranti
cases because all experiments failed to capture ‘extreme’ RI.Nepartak
(26)Meranti(26)
Megi
(21)
Slide50Nepartak02W
forecast
intensity composite
(c)
GSI
(b)GSI
(a)HWRF
(d)H8AMV2
- B216 is too strong in the early
intensification
stage.
- HWRF & HGSI look better than
B216 in the early
intensification
stage. GSI looks slightly better.
- However, a
ll models are too weak in the mature stage and missed RI.
(c)H8AMV
Slide51Along-/cross-track biases
51
Track biases: GSI-AMV2 < GSI-AMV < GSI ~ HWRF=>The more H8AMV were assimilated, the smaller track biases became. 1: synoptic flow, 2: storm
structure through beta-gyre effect.
Mean track errors in the along-/cross-track directions. Dots: mean displacement of storm center from besttrack with a 24 h interval
.All cases (# 72) are averaged.Cross-error bias is significant.
Slide52Meranti16W
forecast
intensity composite
(c)GSI(b)HWRF
(a)B216
(d)HGSI- B216 is too strong in the early intensification
stage. - HWRF & HGSI look better than B216 in the early intensification stage. GSI looks slightly better. - However, a
ll models are too weak in the mature stage and missed RI.
Replacement is needed!!!
Slide53Why did tracks changed?
Track error difference
=
H8AMV2
- HWRF
Nepartak (km)
2016070300
HWRF
H8AMV2
2016070400
HWRF
H8AMV2
2016070218
HWRF
H8AMV2
2016070318
HWRF
H8AMV2
Q: How can we diagnose the factor which causes track change?
Meranti
Megi
Synoptic forecast skill: Z500, steering flows…
Beta gyre effect via TC size
Slide54Forecasted Track biases
54
Mean track errors in the
lon/lat directions. Northward track error bias is significant.Track biases: AMV2 < AMV < GSI ~ HWRF
=>The more Himawari-8 AMVs were assimilated, the fewer track biases became. 1. synoptic flow. 2. storm structure through beta-gyre effect.
Longitude track bias (km)
Northward bias
Southward bias
Latitude track bias (km)
HWRF
GSI
AM
V
AMV2
Dots: mean displacement of storm center from besttrack with a 24 h interval.