VIIRS Contributions to the JPSS Hydrological Initiative

VIIRS Contributions to the JPSS Hydrological Initiative

Andi Walther1, Samantha Tushaus, Andrew Heidinger2

2NOAA/NESDIS/Center for Satellite Applications and Research1University of Wisconsin, CIMSS, Madison Wisconsin

Our role in the JPSS Hydrological Initiative

Measuring precipitation from Satellite

What is the impact of rain droplets on measured radiation?

• In solar spectral range we measure cloud microphysics:• Cloud optical thickness• Cloud top effective radius• Cloud water content

• Do we actually “see” rain from Satellite?• Rain is optically very thin compared to clouds • Since rain is underneath clouds we can’t measure it. Bringing rain into a

volume of view won’t change measured radiance!

• Approach: A conceptual model: • Basic assumption is that cloud microphysics describe processes controlling

formation of cloud droplets, their growth and fallout as precipitation• Cloud droplet size and cloud water content are correlated with precipitation.

What is the impact of rain droplets on radiation?

• In infra-red spectral range we measure:• Cloud top temperature• Cloud top altitude

• Do we actually “see” rain from Satellite?• No (e.g. we can’t discriminate thick anvil cloud from the core of a

thunderstorm)

• How to use IR information: Cloud top temperature is correlated with precipitation ( Thick, cold clouds rain often)

What is the impact of rain droplets on radiation?

• In microwave range rain droplets has an impact on measurement

Rain droplets have direct impact on radiation, but surface rain retrievals are still empirical.Task is to find beta according cloud type, region etc. by comparing to truth data.

(Bennartz )

Ground-based Radar measurements

• NEXRAD CONUS mosaic has 2 minutes / 1km coverage

Radar measurements

Radar provides direct signal, but also:• Reflectivity / surface

rain ratio (Z/R relation) has a wide possible range

• Radar measures in different altitudes and volume sizes

• Coverage is not global, radar sites are expensive

Recap

• IR sees cloud top. Solar sees cloud thickness, particle size and cloud water path. Both are correlated with rain but no direct signal

• Direct signal of precipitation only observed in the microwave• Ground-based radars allow do derive much more information about

precipitation, but much more expensive and have their own issues)

• Most directly linked to surface precipitation

• Over cold (water) surfaces only

• All types of surfaces• More indirect

Cloud property retrievals

DCOMP- The NOAA Enterprise Daytime Cloud Optical & Microphysical Properties Algorithm

• Standard approach; King 1987 and Nakajima & King 1990:

• Retrieve Cloud particle size (effective radius) and Cloud optical depth using two channels in VIS and NIR.

• Retrieve liquid water path assuming vertically homogeneity:

LWP = 2/3 COD * REF

• Different Look-up-tables for ice and water. We cooperate with Ping Yang and Brian Baum.

• Image shows that simultaneous retrieval of COD and REF is theoretically possible above a certain particle size.

Optical Properties: DCOMP

• Standard approach for DCOMP simultaneous measurements in a visible and in an absorbing Near-IR channel (VIIRS M-12 channel at 3.75 micron)

• Use of solar reflectance, solar irradiance is known very accurately.• NIR channel is in mixed solar/terrestrial region of spectrum around

3.8 micron.• Forward model equation set for a given geometrical constellation

is:

Optical Properties: Nighttime/NLCOMP

• Standard approach for NLCOMP simultaneous measurements in a visible (DNB) and in an absorbing Near-IR channel (VIIRS M-12 channel at 3.75 micron)

• Use of lunar reflectance. Lunar irradiance is not known accurately.• NIR channel is in mixed solar/terrestrial region of spectrum around

3.8 micron.• Forward model equation set for a given geometrical constellation

is:

DCOMP Examples (STAR JPSS EDR Monitoring)

Joint COD/CTP histogram for 10 days of data from January 2015

Verification of DCOMP/CLAVR-x Liquid Water Path (LWP) by Comparison to MODIS

DCOMP/CLAVR-x processes both VIIRS, AVHRR and MODIS, so a direct comparison is possible.

• While we are reporting large errors in LWP.

• These come from physical shortcomings shared by all VIS/NIR retrievals.

• This shows a comparison to NASA MODIS products – and our agreement is very good.

VIS/NIR Precipitation Retrieval

Precipitation retrievals from VIS/NIR approach

• Precipitation estimates from passive imagers are based on conceptual models, which relate cloud (top) physical properties and cloud water path to precipitation falling from a cloud.

• Measurements in VIS/NIR channels are not sensitive to rain drops and surface precipitation. Be careful with interpretation of the results!

• Assumptions: • Cloud water path is proportional to probability of rain fall. • Cloud droplets have to have a certain size for onset of precipitation.

• Retrievals apply statistical methods which use truth data (often ground-based radar data) during development to link cloud properties with precipitation.

VIIRS Sensitivity studies: CWP/REF/COD• Matching 5 days of VIIRS cloud properties with NEXRAD surface

precipitation.• Images show NEXRAD precipitation above different thresholds

frequency as a function of DCOMP/VIIRS cloud properties

VIIRS sensitivity studies: CWP/Rain Column

• Rain column: assumed geometrical cloud thickness of precipitating cloud. Computed with warmest cloud top in 20km area.

• Assumption: The smaller the cloud geometrical thickness, the higher rain rate.

VIIRS sensitivity studies: COD/CTT

Conclusions:Comparison to ground-based data show that cloud properties hold some information content to detect rain and to estimate rain rates.

Precipitation Retrieval from VIS/NIR approach

• [Wentz and Spencer,1990] and [Roebeling and Hollemann, 2011] developed rain rate estimate techniques which rely on cloud water path and rain column height estimated by cloud top temperature at daytime

• Rain Column: Height in km:

• CWP to Rain Rate R:

Precipitation Retrieval from VIS/NIR approach

• [Wentz and Spencer,1990] and [Roebeling and Hollemann, 2011] developed rain rate estimate techniques which rely on cloud water path and rain column height estimated by cloud top temperature at daytime

• Rain Column Height in km:

• CWP to Rain Rate R:

Finding thresholds for rain/no-rain discrimination

• Use of NEXRAD area rain fraction to find optimal thresholds of CWP and REF. Below these values rain is excluded.

NEXRAD average

CLAVR-x

REF_t=20um

• Optimizing threshold values using several days of test data.

• Optimized coefficients may vary due to region, season, weather and cloud type ( strati form vs. convective)

CWP_t=560 mm-2

NEXRAD average

Precipitation retrieval

Example:Texas flood

case 18 April 2016

NEXRAD Precipitation composite

Resolution:• 0.02 degree

(~1km)• 2 minutes

Cloud Effective Radius

Cloud Water PathRain RateNEXRAD

Rain Detection and Rate

NEXRAD VIS/NIR

Detection thresholds for CONUS:Day: CWP_T = 560 mm-2 ; REF_T = 18 μmNight: CWP_T = 480 mm-2 ; REF_T = 14 μm

Comparing VIS/NIR approach nighttime rain rate with MW-based MIRS rain rate from ATMS/NPP

NLCOMP may fill Arctic winter observation gap for rain rate and

cloud parameters.

NLCOMP cloud products and rain rate estimates

ATMS sensor on NPP provides MW–based rain rate

- less physical based retrieval + much higher spatial resolution ( 750m vs 35km )Q.: Is a hybrid algorithm feasible?

Day-Night consistency of rain rate

Rain rate estimates from VIS/NIR approach are largely consistent to microwave-based retrievals. Thresholds for nighttime seems to be too high

Rain retrieval: Summary and outlook

• CLAVR-x algorithm system provides rain detection and rain rate products from a VIS/NIR approach for all current sensors.

• Rain retrieval runs also at nighttime with DNB/VIIRS channel if moon phase allows.

• Rain products are part of CSPP/CLAVR-x software package

Next steps will focus on • adjusting rain discrimination thresholds for different regions

and cloud/weather types.• evaluation, validation• Developing of hybrid rain retrievals with VIIRS/ATMS both on

NPP; challenge is different spatial resolution and different information skill according cloud type and situation.

Towards a Spectrally Consistent Liquid Water Path from VIIRS

Andrew Heidinger, Andi Walther and Sam Tushaus

Outline

• VIIRS and the NOAA Enterprise DCOMP• Importance of LWP• The Spectral Inconsistency Issue• Comparisons with AMSR2 LWP• Deriving a spectrally-consistent LWP from VIIRS

DCOMP – The NOAA Enterprise Daytime Cloud Optical & Microphysical Properties Algorithm

• Standard approach; King 1987 and Nakajima & King 1990:

• Retrieve Cloud particle size (effective radius) and Cloud optical depth using two channels in VIS and NIR.

• Retrieve liquid water path assuming vertically homogeneity:

• Different Look-up-tables for ice and water. We cooperate with Ping Yang and Brian Baum.

• Image shows that simultaneous retrieval of COD and REF is theoretically possible above a certain particle size.

DCOMP on VIIRS• DCOMP is a bispectral approach.

• One channel is a non-absorbing channel and Is always 0.65 mm.

• The second channel has particle absorption.

• M8 – 1.6 μm• M11 - 2.2 μm• M12 – 3.75 μm

• VIIRS also provides the unique ability to run DCOMP at the I-band resolution using I1 + I3 & I4. (A. Walther is developing this in CLAVR-x)

DCOMP Examples (STAR JPSS EDR Monitoring)

Verification of DCOMP/CLAVR-x Liquid Water Path (LWP) by Comparison to MODIS

DCOMP/CLAVR-x processes both VIIRS, AVHRR and MODIS, so a direct comparison is possible.

• While we are reporting large errors in LWP.

• These come from physical shortcomings shared by all VIS/NIR retrievals.

• This shows a comparison to NASA MODIS products – and our agreement is very good.

Why Care about LWP from VIIRS?

• LWP is key cloud parameter and included in the GCOS list of ECVs (Essential Climate Variables)

• VIIRS is more sensitive than m-wave radiometers to small amounts of LWP that are still very important to the radiation budget (small amounts of LWP contributed a lot to the albedo)

• VIIRS can measure LWP well over snow-free land.• LWP varies significantly at fine spatial scales and VIIRS is best suited to

study this.• Previous studies of these issues always use MODIS Science Team Data

– application of the NOAA Enterprise Cloud Products is a good test of their physical consistency.

The Physical Problem

https://www.arm.gov/publications/proceedings/conf10/extended_abs/ovtchinnikov_m.pdf

• Cloud particle sizes vary with height through the cloud (left image)

• The depth into the cloud observed by the 3 DCOMP absorbing channels varies so that the effective radii vary from each channel for the same cloud (right)

The Problem Contd.

https://www.wmo.int/pages/prog/gcos/documents/SatelliteSupplement2011Update.pdf

• The spectral variation (i.e. which channel combination you use ) has big impact on CWP and LWP.

• Image on the right shows LWP from DCOMP using 1.6 vs 3.75 microns.

• Those differences are about 20% which is about the accuracy limit from GCOS.

• Compared to AMSR2, the errors are even bigger.• Therefore, this issue is limiting the utility of VIIRS LWP.

Example of Spectral Variation of reff for one scene

This image cannot currently be displayed.

• Spectral variation in LWP comes from spectral variation in reff• COD variations are much less

reff from 1.6 μm reff from 2.2 μm reff from 3.75 μm

Previous Work• These issues are well-known.• Others have attempted to retrieve particle size

profiles.• Others have studied these spectral difference in the

context of adiabatic models and the role of drizzle.• The 3D Radiative Transfer Community also studies

these phenomena.

Our Goal• Derivation of an adjustment to correct DCOMP

output for these issues. (This seems to be missing in the above studies)

Comparison to AMSR2

AMSR2 Comparison• We use JAXA/GCOM/AMSR2 products with a resolution of 0.25 degrees• We process AQUA/MODIS (a VIIRS surrogate) since both are in the A-train• AMSR2 LWP is missing over land and heavy precip (left image)• DCOMP LWP (right image) was filtered to remove land, ice contamination and snow surfaces• DCOMP was filtered for CWP > 300 g/m^2 to avoid saturation issues.• Analysis done for all of July 2013

Spectral Inconsistency in AMSR2 Comparisons

1.6 μm 2.2 μm 3.75 μm

• None of the DCOMP LWP values matches AMSR2• Biases are less for 3.75 μm• Correlations similar• If AMSR2 is true, DCOMP fails GCOS requirements and VIIRS Spec (25%).

The Solution, Part 1: Estimation of LWP-consistent ReffAKA – If we assume m-wave LWP and VIS/NIR COD are correct, can we derived an Reff that is reconciles m-wave and VIS/NIR LWP?

Question

• If we assume m-wave LWP and VIS/NIR COD are correct, can we derive an reff that reconciles μ-wave and VIS/NIR LWP?

Method

LWP = (2/3) * COD * reff (standard equation)

LWP = (5/9) *COD * reff,top (adiabatic equation)

We take the standard equation and derive reff for each VIS/NIR COD and m-wave LWP

Image on shows the mean reff derived for July 2013.

Comparison of μ-wave and DCOMP reff

Comparison of m-wave and DCOMP reff

The Solution, Part 2: Estimation of Spectrally-consistent reff

Method

• Colocation with AMSR2 LWP with VIIRS COD has generated a new values of reff that improves the LWP calculation (reff,fit)

• Can we predict reff from the 3 reff values from the 3 NIR spectral channels available on VIIRS, MODIS and GOES-R?

• Theory would predict that the 3 reff values would vary according to the reff and extinction profiles in the cloud.

• We regress reff,fit as a function of reff + Δreff + (Δreff)2 for each combination.

• Here is the comparison of the reff,fit against the reff derived from matching AMSR2 LWP and DCOMP COD.

• Here is the comparison of the LWP derived from reff,fit against the reff derived from matching AMSR2 LWP.

• The fit acheives its goal.

What if we have just 1 reffmeasurement? (i.e. AVHRR)

• It is worse than the 3-reff solution (as expected)

• But the bias is still low

• Certainly worth doing to fix biases in the AVHRR record like in PATMOS-x or ISCCP

Impact of Preciptation

AMSR2 Precipitation in the Filtered Data Set.

• Most precipitation was removed by the filters placed on DCOMP to remove ice clouds and thick water clouds.

Impact of Precipitation on Reff from AMSR2

• Areas of higher reff derived from AMSR2 LWP and DCOMP COD show a correspondence with precipitation from AMSR2.

Impact of Precipitation• Without precip, the ratio of the AMSR

reff,fit to the DCOMP 3.75 μm reff was about 0.5

• This means DCOMP LWP was about twice as big as AMSR2 LWP

• As soon we include pixels with precipitation from AMSR2, the situation changes. DCOMP reff is too small and observed LWP is too low.

• This is bad news for LWP but demonstrates the DCOMP does have a sensitivity to precipitation that we are exploiting.

Conclusions

• VIIRS is an excellent sensor for DCOMP applications.• The physics of the observations impose a spectral inconsistency on

the DCOMP LWP values.• We used AMSR2 LWP to derive a method that can fix this LWP bias

and bring the LWP accuracy closer to the GCOS requirements and the VIIRS product specifications.

• This method fails for the small % of the globe with precipitation but DCOMP is using this information to derive precip information that should complement the IR and μ-wave.