Nowcasting of stratiform rain exploiting radar signatures in the dendritic growth layer

(1)

Nowcasting of stratiform rain exploiting radar signatures in the dendritic growth layer

Silke Trömel

Institute for Geosciences, University Bonn

(2)

RealPEP

Hypothesis

Polarimetric radar measurements enable the detection of snow generation at high altitudes and thus nowcasting with lead times of 30 minutes and more.

Between -10°C and -20°C optimal conditions for ice growth are

encountered

(Dendritic Growth Layer, DGL).

Background

(3)

RealPEP

Polarimetric radar measurements

• 27 stratiform rain events (> 3.5 h) obs. 2013 – 2016 with BoXPol (pol. X-band radar in Bonn)

• widespread, significant reflectivities

Surface reflectivities Z

_H

on 7 October 2014, 0:00-3:30 UTC

(4)

RealPEP

Quasi-Vertical-Profiles (QVPs)

Step 1:

Step 3:

Z

_H

Source: Modified after Kumjian and Lombardo (2017)

Step 2: _H ^ei ^gh

t

Time

H e ig h t

(5)

RealPEP 7 October 2014: QVPs

Z

_DR

: high for very anisotropic crystals

Z

_DR

Z

_H

Z

_H

: increase in aggregation processes

H e ig h t [k m ] H ei gh t [k m ]

K

_DP

: high for high concentrations of early, still anisotropic aggregates

K

_DP

H ei gh t [k m ]

(6)

RealPEP

ML

Nowcasting Strategy

DGL

Fall velocity/

Temperature/ Height

12°C 0°C/

2 km -12°C/

4 km

1 m/s 10 m/s

Wind speed -15°C/

4.5 km

IWC 0.7 g/m³ Temperature/ Height

LWC / 0.07 g/m³

Assumption:

Precipitation flux does not change much

Neglect

• Depositional growth

• Riming

• Sublimation

• Evaporation

(7)

RealPEP

1. Ice water content (IWC) and snow rate (S) in the DGL - Analysis of QVPs for 27 stratiform rain events (> 3.5 h) -

[mm/h]

[g/m³]

Bukovčić et al. 2019

S(

Nowcasting Strategy

(8)

RealPEP 7 October 2014: QVPs

H ei gh t [k m ]

K

_DP

H e ig h t [k m ]

Z

_DR

Z

_H

H ei gh t [k m ]

Time series of 90 percentiles of Z

_H

(DGL), Z

_DR

(DGL), K

_DP

(DGL)

between the -10

^o

C and -20

^o

C isotherms

(9)

RealPEP

1. Ice water content (IWC) and snow rate (S) in the DGL 2. Snow-/rain trajectories exploiting radial winds (VAD)

- Analysis of QVPs for 27 stratiform rain events (> 3.5 h) -

Nowcasting Strategy

(10)

RealPEP

1. Ice water content (IWC) and snow rate (S) in the DGL 2. Snow-/rain trajectories exploiting radial winds (VAD)

3. Correlation between K _DP (DGL) and Z _H (surface) at estimated location determines lead time

- Analysis of QVPs for 27 stratiform rain events (> 3.5 h) -

Nowcasting Strategy

(11)

RealPEP

1. Ice water content (IWC) and snow rate (S) in the DGL 2. Snow-/rain trajectories exploiting radial winds (VAD)

3. Correlation between K _DP (DGL) and Z _H (surface) at estimated location determines lead time

4. Compare S(IWC) in DGL with surface rain rate a. measured by rain gauges nearby

b. estimated from surface reflectivities using z _H =72R _Bo ^2.14

at the estimated location and taking the lead time into account - Analysis of QVPs for 27 stratiform rain events (> 3.5 h) -

Nowcasting Strategy

(12)

RealPEP

• Trajectory analysis: Snow horizontally advected by 41.10 km

• Correlation (K _DP (DGL), Z _H (Surface)) = 0.75

• Lead time = 50 min.

Results for 7 October 2014

(13)

RealPEP

• Trajectory analysis: Snow horizontally advected by 23.11 km

• Correlation (K _DP (DGL), Z _H (Surface)) = 0.8

• Lead time = 30 min.

Results for 12 April 2013

(14)

RealPEP

Results

• Average correlation between K _DP (DGL) and Z _H (surface) for the 27 rain events considered is r=0.57;

• Other relevant factors:

– neglected depositional growth, riming, sublimation, evaporation – wind variability

– spatial degradation of K _DP -signals using QVPs

– neglected attenuation

(15)

RealPEP

Results

• Average correlation between K _DP (DGL) and Z _H (surface) for the 27 rain events considered is r=0.57;

• Other relevant factors:

– neglected depositional growth, riming, sublimation, evaporation – wind variability

– spatial degradation of K _DP -signals using QVPs – neglected attenuation

• DGL-based S(IWC) estimates mostly follow the ensuing surface rain rates at the estimated location

• Correlations up to 0.8 and lead times up to 120 min. indicate the

potential of the nowcasting strategy

(16)

RealPEP

Results

• Average correlation between K _DP (DGL) and Z _H (surface) for the 27 rain events considered is r=0.57;

• Other relevant factors:

– neglected depositional growth, riming, sublimation, evaporation – wind variability

– spatial degradation of K _DP -signals using QVPs – neglected attenuation

• DGL-based S(IWC) estimates mostly follow the ensuing surface rain rates at the estimated location

• Correlations up to 0.8 and lead times up to 120 min. indicate the

potential of the nowcasting strategy

(17)

RealPEP

Ongoing/ future work

• Rain rate measured at the surface is mostly higher than the snow flux estimated at the DGL level.

• Even relatively small LWP frequently observed means serious riming that changes IWC and mass flux of snow (or snow rate) significantly.

This has to be taken into account when projecting the results of

polarimetric snow quantification at the DGL down to the surface.

(18)

RealPEP

The impact of riming for pure snow events

Exploiting measurements of

• polarimetric radars for IWC retrievals aloft

• Passive microwave radiometers for liquid water path (LWP)

• pluvios (weighing rain gauges) for surface precipitation measurements Assumption:

All SLW is located below the DGL

BoXPol, 10 December 2017

9:00-12:30 UTC

(19)

RealPEP

Snow case: 10 December 2017

K

_DP

Z

_DR

Z

_H

Log10(IWC)

IWC=10

^-0.1

=0.79g/m³ S(IWC)=3.7·IWC=2.9 mm/h

Acc. precip over 3h  9mm

(20)

10 December 2017- ct‘d

DGL-estimate: 9 mm vs.

12 mm measured

(21)

Riming impact

LWC[ LWP [

Mass of supercooled water M

_r

that each ice particle with equivolume diameter D and initial mass M

_i

collects on the way from the DGL to the surface:

Initial mass M

_i

of the ice particle:

�

_�

[ � ]= �

6 10

⁻³

� �

²

[�� ] with a between 0.1 and 0.2

�

_�

�

_�

=1.5 ∙ 10

⁻³

�

_�

��

�

collection efficiency E

_r

may vary in a wide range from 0 to 0.8 and depends on the unkown size of cloud droplets (Moisseev et al. 2017)

Note: M

_r

/M

_i

ratio does not depend on PSD

and is entirely determined by the LWP!

(22)

10 December 2017

Liquid water path (LWP)

measured with microwave radiometer (HATPRO)

LWP is about 100 g/m

²

and then sharply increases towards the end of event.

(23)

Riming impact

LWC[ LWP [

Mass of supercooled water M

_r

that each ice particle with equivolume diameter D and initial mass M

_i

collects on the way from the DGL to the surface:

Initial mass M

_i

of the ice particle:

�

_�

[ � ]= �

6 10

⁻³

� �

²

[�� ] with a between 0.1 and 0.2

�

_�

�

_�

=1.5 ∙ 10

⁻³

�

_�

��

�

with E

_r

=0.5, a=0.1, and LWP=134g/m², the snow mass doubles;

with E

_r

=0.5, a=0.1, and LWP=100g/m², the snow mass increases by factor 1.75;

(24)

Conclusions on the multi-sensor study

• Example case 10 December 2017: It is quite likely that riming adds a significant mass to snow, we may attribute the underestimation (9 mm vs 12 mm) to the impact of riming.

Nowcasting of stratiform rain exploiting radar signatures in the dendritic growth layer

Nowcasting of stratiform rain exploiting radar signatures in the dendritic growth layer

Silke Trömel

Institute for Geosciences, University Bonn

RealPEP

Hypothesis

Polarimetric radar measurements enable the detection of snow generation at high altitudes and thus nowcasting with lead times of 30 minutes and more.

Between -10°C and -20°C optimal conditions for ice growth are

encountered

(Dendritic Growth Layer, DGL).

Background

RealPEP

Polarimetric radar measurements

• 27 stratiform rain events (> 3.5 h) obs. 2013 – 2016 with BoXPol (pol. X-band radar in Bonn)

• widespread, significant reflectivities

Surface reflectivities Z

on 7 October 2014, 0:00-3:30 UTC

RealPEP

Quasi-Vertical-Profiles (QVPs)

Step 1:

Step 3:

Z

Source: Modified after Kumjian and Lombardo (2017)

Step 2: H ei gh

t

Time

H e ig h t

RealPEP 7 October 2014: QVPs

Z

: high for very anisotropic crystals

Z

Z

Z

: increase in aggregation processes

H e ig h t [k m ] H ei gh t [k m ]

K

: high for high concentrations of early, still anisotropic aggregates

K

H ei gh t [k m ]

RealPEP

ML

Nowcasting Strategy

DGL

Fall velocity/

Temperature/ Height

12°C 0°C/

2 km -12°C/

4 km

1 m/s 10 m/s

Wind speed -15°C/

4.5 km

IWC 0.7 g/m³ Temperature/ Height

LWC / 0.07 g/m³

Assumption:

Precipitation flux does not change much

Neglect

• Depositional growth

• Riming

• Sublimation

• Evaporation

RealPEP

1. Ice water content (IWC) and snow rate (S) in the DGL - Analysis of QVPs for 27 stratiform rain events (> 3.5 h) -

[mm/h]

[g/m³]

[g/m³]

[g/m³]

Bukovčić et al. 2019

S(

Nowcasting Strategy

RealPEP 7 October 2014: QVPs

H ei gh t [k m ]

K

H e ig h t [k m ]

Z

Z

H ei gh t [k m ]

Time series of 90 percentiles of Z

(DGL), Z

(DGL), K

(DGL)

Step 2: _H ^ei ^gh

3. Correlation between K _DP (DGL) and Z _H (surface) at estimated location determines lead time

3. Correlation between K _DP (DGL) and Z _H (surface) at estimated location determines lead time

b. estimated from surface reflectivities using z _H =72R _Bo ^2.14

• Correlation (K _DP (DGL), Z _H (Surface)) = 0.75

• Correlation (K _DP (DGL), Z _H (Surface)) = 0.8

• Average correlation between K _DP (DGL) and Z _H (surface) for the 27 rain events considered is r=0.57;

– spatial degradation of K _DP -signals using QVPs

• Average correlation between K _DP (DGL) and Z _H (surface) for the 27 rain events considered is r=0.57;

– spatial degradation of K _DP -signals using QVPs – neglected attenuation

• Average correlation between K _DP (DGL) and Z _H (surface) for the 27 rain events considered is r=0.57;

– spatial degradation of K _DP -signals using QVPs – neglected attenuation