Lukla CNR4 Net Radiometer

The CNR 4 net radiometer hes been installed in Lukla in 2013 and it consists of a pyranometer pair, one facing upward, the other facing downward, and a pyrgeometer pair in a similar conﬁguration. The pyranometer pair measures the short-wave radiation. And the pyrgeometer pair measures long-wave radiation. The upper long-wave detector of CNR 4 has a meniscus dome. This ensures that water droplets role off easily and improves the field of view to nearly 180°, compared with a 150° for a flat window. All 4 sensors are integrated directly into the instrument body, instead of separate modules mounted onto the housing. But are each calibrated individually for optimal accuracy.

Two temperature sensors, a Pt-100 and Thermistor, are integrated for compatibility with every data logger. The temperature sensor is used to provide information to correct the infrared readings for the temperature of the instrument housing. Care has been taken to place the long-wave sensors close to each other and close to the temperature sensors. This assures that the temperatures of the measurement surfaces are the same and accurately known. Which improves the quality of the long-wave measurements.

Technical Characteristics:

Spectral range: 300 to 2800 (short wave) nm

Spectral range: 4500 to 42000 (long wave) nm

Sensitivity: 5 to 20 µV/W/m²

Temperature dependence of sensitivity (-10 ºC to +40 ºC) : < 4 %

Response time: < 18 s

Non-linearity: < 1 %

Operating temperature: -40 to 80 °C

Ventilation power (of the optional CNF 4 ventilation unit): 10 W

Citation proposal
(2014) . Lukla CNR4 Net Radiometer. https://geoportal.mountaingenius.org/geonetwork/srv/api/records/a637953d-030f-48e6-951e-41805ca8b610

Simple

Date ( Publication ): 2014-09-04T19:08:00

Edition

Purpose: The CNR 4 net radiometer measures the energy balance between incoming short-wave and long-wave Far Infrared (FIR) radiation versus surface-reﬂected short-wave and outgoing long-wave radiation

Status: Completed

Point of contact

Ev-K2-CNR - ( )

Via San Bernardino 145 Bergamo BG 24126 Italy

+39 035 3230511

+39 035 3230551

Website: http://www.evk2cnr.org/

Maintenance and update frequency: As needed

Keywords ( Theme )

NEXTDATA

Keywords ( Theme )

NEXTDATA

Keywords ( Theme )

SHARE , AWS station , monitoring network ,

Keywords ( Place )

Nepal , Lukla , Khumbu Valley , Sagarmatha National Park

Keywords ( Theme )

NEXTDATA

Keywords ( Theme )

SHARE , AWS station , monitoring network ,

Keywords ( Place )

Nepal , Lukla , Khumbu Valley , Sagarmatha National Park

Keywords ( Theme )

NEXTDATA

Keywords ( Theme )

SHARE , AWS station , monitoring network ,

Keywords ( Place )

Nepal , Lukla , Khumbu Valley , Sagarmatha National Park

Keywords ( Theme )

NEXTDATA

Keywords ( Theme )

SHARE , AWS station , monitoring network ,

Keywords ( Place )

Nepal , Lukla , Khumbu Valley , Sagarmatha National Park

Use limitation: Data Sharing and Publication Policy at DISTRIBUTION SECTION

Access constraints: Intellectual property rights

Other constraints

Spatial representation type: Vector

Character set: UTF8

Topic category

Climatology, meteorology, atmosphere
Geoscientific information
Environment

Reference system identifier: EPSG / WGS 84 (EPSG:4326) / 7.4

Topology level: Geometry only

Geometric object type: Point

Geometric object count: 1

OnLine resource: SHARE Geonetwork
SHARE Geonetwork

OnLine resource: gn:share_station
Show geographic position

OnLine resource: View in Google Earth
View in Google Earth

OnLine resource: https://geoportal.mountaingenius.org/geonetwork/srv/api/records/a637953d-030f-48e6-951e-41805ca8b610/attachments/Data%20Usage%20Policy.pdf

Hierarchy level: Series

Other: Instruments: AWS sensors

Statement

The automatic weather stations (AWS) for monitor meteorological conditions are currently included in the SHARE network.

An automatic weather station (AWS) is defined as a “meteorological station at which observations are made and transmitted automatically” (WMO, 1992a).

In particular, in the framework of the SHARE project for all parameters, the data has been visually checked, looking for extremely and unusual low/high values and/or periods with constant values.